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Applications of Body Worn Sensors and Wearables

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 116316

Special Issue Editor

Prof. Dr. Bijan Najafi

E-Mail Website
Guest Editor
Baylor College of Medicine, 7200 Cambridge Street, Rm B01.532 (iCAMP), Houston, TX, USA
Interests: wearable technology; digital health; fall prevention; cognitive impairment; exergame; gamification; diabetes care; diabetic foot; wound healing; telehealth; dementia; peripheral vascular disease; movement science; mobile health; population health; aging in place and well-built
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various aspects of the economic burden of many healthcare systems in both developed and developing countries are associated with an aging population, in which 70% will need some kind of long-term care. This, of course, is exacerbated by the fact that our population continues to age and live longer. For instance, it is estimated that in the USA alone, more than 10,000 Americans reach Medicare age every day, some of whom will develop multiple chronic conditions and account for a large share of Medicare spending. Management of chronic diseases requires patient behavior change, and thus, a greater emphasis must be placed on the patient’s central and active role. This constitutes an important shift in current clinical practice. At present, systems relegate the patient to the role of a passive recipient of care, thereby missing the opportunity to leverage what they can do to promote personal health. Healthcare for chronic conditions must be reoriented around the patient and caregivers. In addition, more emphasis should be allocated to self-managed prevention at individual level.

The COVID-19 pandemic also exposes our health care system’s weaknesses. For instance, this outbreak shows that traditional healthcare delivery models for managing chronic illness are not at scale to handle situations like the global COVID-19 crisis. Because people with chronic illness and older adults represent fragile populations, it is recommended to avoid unnecessary hospital admissions to reduce the risk of COVID-19 exposure in the hospital. This is disrupting the best practices for preventive or timely care for older adults and those with chronic illness.

The widespread uptake and acceptance of technology represents an opportunity to address this rising challenge. In particular, thanks to advances in wearables and digital health technologies, this is an opportunity to empower patients and/or their caregivers to be engaged as a part of the healthcare ecosystem. However, there are still fundamental gaps in adopting such technologies for the management of chronic illness and preventive care. For instance, while advanced signal processing, artificial intelligence, and remote monitoring have transformed the landscape of digital health industries, it is still unclear what clinically meaningful information could be extracted from these technologies to enable healthcare professionals to provide personalized care, empower patients to take care of their own health, deliver care remotely, and assist caregivers in effectively coordinating care.

This Special Issue is focused on applications of wearables, digital health, and data analytics to facilitate management of chronic conditions or preventive care. Some examples of these applications could be new studies that utilize sensors to extract digital biomarkers associated with cognitive decline, motor capacity deterioration because of specific conditions (e.g., dementia, Parkinson’s disease, stroke, cancer, diabetes), technologies to improve care management for those suffering from chronic conditions (e.g., chronic pain), sensors to improve patient adherence, sensors to better monitor sleep and stress, sensors for remotely monitoring health and wellbeing, sensors to facilitate tracking symptoms related to COVID-19, and sensors to manage environmental conditions that may impact health and wellbeing, such as humidity, temperature, light, CO2, etc. Also of interest are advanced data analytics developed to extract meaningful information from raw sensor data, fuse multimodal data, quantify interaction between physiological systems (e.g., cardiorespiratory and motor systems, emotion/cognition and motor systems), quantify and visualize the pattern of individuals’ behavior in the context of everyday life, and to identify early signs of infection (e.g., COVID-19) and healthy recovery post treatment (e.g., post COVID recovery, post hospital discharge recovery, etc.)

Prof. Dr. Bijan Najafi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearables
  • digital health
  • digital twin
  • Internet of Thing
  • mHealth
  • remote patient monitoring
  • chronic illness
  • precision environment
  • personalized medicine
  • care coordination
  • outcome research
  • fall prevention
  • frailty
  • diabetes
  • cancer
  • aging in place
  • wellness
  • well built
  • exergame
  • dementia
  • cognitive impairment
  • diabetic foot
  • wound healing
  • patient care
  • pain management
  • emotion-sensing
  • daily-life context
  • data analytics
  • behavior patterns
  • human motion biomechanics
  • rehabilitative technologies

Published Papers (43 papers)

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17 pages, 1952 KiB  
Article
Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer
by Arian Skoki, Boris Gašparović, Stefan Ivić, Jonatan Lerga and Ivan Štajduhar
Sensors 2024, 24(5), 1700; https://0-doi-org.brum.beds.ac.uk/10.3390/s24051700 - 06 Mar 2024
Viewed by 652
Abstract
Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and [...] Read more.
Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players’ responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 5705 KiB  
Article
Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject’s Gait Progression Using Wearable Inertial Sensor
by Haneul Jeon and Donghun Lee
Sensors 2024, 24(4), 1276; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041276 - 17 Feb 2024
Cited by 1 | Viewed by 619
Abstract
Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference [...] Read more.
Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model’s recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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19 pages, 6741 KiB  
Article
Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study
by Luisa Luebke, Philip Gouverneur, Tibor M. Szikszay, Wacław M. Adamczyk, Kerstin Luedtke and Marcin Grzegorzek
Sensors 2023, 23(19), 8231; https://0-doi-org.brum.beds.ac.uk/10.3390/s23198231 - 03 Oct 2023
Cited by 1 | Viewed by 1208
Abstract
Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more [...] Read more.
Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode. Different sensors measured physiological responses. Machine learning models were trained to evaluate performance in distinguishing pain levels and identify key sensors and features for the classification task. The results show that distinguishing between no and severe pain is significantly easier than discriminating lower pain levels. Electrodermal activity is the best marker for distinguishing between low and high pain levels. However, recursive feature elimination showed that an optimal subset of features for all modalities includes characteristics retrieved from several modalities. Moreover, the study’s findings indicate that differences in physiological responses to pain in HS and CBPP remain small. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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21 pages, 2181 KiB  
Article
Telerehabilitation with ARC Intellicare to Cope with Motor and Respiratory Disabilities: Results about the Process, Usability, and Clinical Effect of the “Ricominciare” Pilot Study
by Marianna Capecci, Rossella Cima, Filippo A. Barbini, Alice Mantoan, Francesca Sernissi, Stefano Lai, Riccardo Fava, Luca Tagliapietra, Luca Ascari, Roberto N. Izzo, Maria Eleonora Leombruni, Paola Casoli, Margherita Hibel and Maria Gabriella Ceravolo
Sensors 2023, 23(16), 7238; https://0-doi-org.brum.beds.ac.uk/10.3390/s23167238 - 17 Aug 2023
Viewed by 1437
Abstract
Background: “Ricominciare” is a single-center, prospective, pre-/post-intervention pilot study aimed at verifying the feasibility and safety of the ARC Intellicare (ARC) system (an artificial intelligence-powered and inertial motion unit-based mobile platform) in the home rehabilitation of people with disabilities due to respiratory or [...] Read more.
Background: “Ricominciare” is a single-center, prospective, pre-/post-intervention pilot study aimed at verifying the feasibility and safety of the ARC Intellicare (ARC) system (an artificial intelligence-powered and inertial motion unit-based mobile platform) in the home rehabilitation of people with disabilities due to respiratory or neurological diseases. Methods. People with Parkinson’s disease (pwPD) or post-COVID-19 condition (COV19) and an indication for exercise or home rehabilitation to optimize motor and respiratory function were enrolled. They underwent training for ARC usage and received an ARC unit to be used independently at home for 4 weeks, for 45 min 5 days/week sessions of respiratory and motor patient-tailored rehabilitation. ARC allows for exercise monitoring thanks to data from five IMU sensors, processed by an AI proprietary library to provide (i) patients with real-time feedback and (ii) therapists with information on patient adherence to the prescribed therapy. Usability (System Usability Scale, SUS), adherence, and adverse events were primary study outcomes. Modified Barthel Index (mBI), Barthel Dyspnea Index (BaDI), 2-Minute Walking Test (2MWT), Brief Fatigue Inventory (BFI), Beck Depression or Anxiety Inventory (BDI, BAI), and quality of life (EQ-5D) were also monitored pre- and post-treatment. Results. A total of 21 out of 23 eligible patients were enrolled and completed the study: 11 COV19 and 10 pwPD. The mean total SUS score was 77/100. The median patients’ adherence to exercise prescriptions was 80%. Clinical outcome measures (BaDI, 2MWT distance, BFI; BAI, BDI, and EQ-5D) improved significantly; no side effects were reported. Conclusion. ARC is usable and safe for home rehabilitation. Preliminary data suggest promising results on the effectiveness in subjects with post-COVID condition or Parkinson’s disease. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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20 pages, 2103 KiB  
Article
Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective
by Mohd Haris Lye, Nouar AlDahoul and Hezerul Abdul Karim
Sensors 2023, 23(15), 6804; https://0-doi-org.brum.beds.ac.uk/10.3390/s23156804 - 30 Jul 2023
Cited by 1 | Viewed by 746
Abstract
Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person’s activities in a consistent [...] Read more.
Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person’s activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP–TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset). Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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16 pages, 3190 KiB  
Article
Validation of In-Shoe Force Sensors during Loaded Walking in Military Personnel
by Pui Wah Kong, Muhammad Nur Shahril Iskandar, Ang Hong Koh, Mei Yee Mavis Ho and Cheryl Xue Er Lim
Sensors 2023, 23(14), 6465; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146465 - 17 Jul 2023
Cited by 2 | Viewed by 1288
Abstract
The loadsol® wireless in-shoe force sensors can be useful for in-field measurements. However, its accuracy is unknown in the military context, whereby soldiers have to carry heavy loads and walk in military boots. The purpose of this study was to establish the [...] Read more.
The loadsol® wireless in-shoe force sensors can be useful for in-field measurements. However, its accuracy is unknown in the military context, whereby soldiers have to carry heavy loads and walk in military boots. The purpose of this study was to establish the validity of the loadsol® sensors in military personnel during loaded walking on flat, inclined and declined surfaces. Full-time Singapore Armed Forces (SAF) personnel (n = 8) walked on an instrumented treadmill on flat, 10° inclined, and 10° declined gradients while carrying heavy loads (25 kg and 35 kg). Normal ground reaction forces (GRF), perpendicular to the contact surface, were simultaneously measured using both the loadsol® sensors inserted in the military boots and the Bertec instrumented treadmill as the gold standard. A total of eight variables of interest were compared between loadsol® and treadmill, including four kinetic (impact peak force, active peak force, impulse, loading rate) and four spatiotemporal (stance time, stride time, cadence, step length) variables. Validity was assessed using Bland–Altman plots and 95% Limits of Agreement (LoA). Bias was calculated as the mean difference between the values obtained from loadsol® and the instrumented treadmill. Results showed similar force-time profiles between loadsol® sensors and the instrumented treadmill. The bias of most variables was generally low, with a narrow range of LoA. The high accuracy and good agreement with standard laboratory equipment suggest that the loadsol® system is a valid tool for measuring normal GRF during walking in military boots under heavy load carriage. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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27 pages, 9425 KiB  
Article
Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial Sensors
by Kaori Fujinami, Ryo Takuno, Itsufumi Sato and Tsuyoshi Shimmura
Sensors 2023, 23(11), 5077; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115077 - 25 May 2023
Cited by 2 | Viewed by 1261
Abstract
Recently, animal welfare has gained worldwide attention. The concept of animal welfare encompasses the physical and mental well-being of animals. Rearing layers in battery cages (conventional cages) may violate their instinctive behaviors and health, resulting in increased animal welfare concerns. Therefore, welfare-oriented rearing [...] Read more.
Recently, animal welfare has gained worldwide attention. The concept of animal welfare encompasses the physical and mental well-being of animals. Rearing layers in battery cages (conventional cages) may violate their instinctive behaviors and health, resulting in increased animal welfare concerns. Therefore, welfare-oriented rearing systems have been explored to improve their welfare while maintaining productivity. In this study, we explore a behavior recognition system using a wearable inertial sensor to improve the rearing system based on continuous monitoring and quantifying behaviors. Supervised machine learning recognizes a variety of 12 hen behaviors where various parameters in the processing pipeline are considered, including the classifier, sampling frequency, window length, data imbalance handling, and sensor modality. A reference configuration utilizes a multi-layer perceptron as a classifier; feature vectors are calculated from the accelerometer and angular velocity sensor in a 1.28 s window sampled at 100 Hz; the training data are unbalanced. In addition, the accompanying results would allow for a more intensive design of similar systems, estimation of the impact of specific constraints on parameters, and recognition of specific behaviors. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 2185 KiB  
Article
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring
by Catherine Park, Mohammad Dehghan Rouzi, Md Moin Uddin Atique, M. G. Finco, Ram Kinker Mishra, Griselda Barba-Villalobos, Emily Crossman, Chima Amushie, Jacqueline Nguyen, Chadi Calarge and Bijan Najafi
Sensors 2023, 23(10), 4949; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104949 - 21 May 2023
Cited by 10 | Viewed by 2058
Abstract
Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify [...] Read more.
Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident with 1-min time resolution. A total of 119 aggression episodes, lasting 7.3 ± 13.1 min for a total of 872 1-min epochs including 132 physical aggression epochs, were collected. The model achieved high precision (80.2%), accuracy (82.0%), recall (85.0%), F1 score (82.4%), and area under the curve (89.3%) to distinguish physical aggression epochs. The sensor-derived feature of vector magnitude (faster triaxial acceleration) was the second contributing feature in the model, and significantly distinguished aggression and non-aggression epochs. If validated in larger samples, this model could provide a practical and efficient solution for remotely detecting and managing aggressive incidents in children. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 2949 KiB  
Article
Symmetry of the Neck Muscles’ Activity in the Electromyography Signal during Basic Motion Patterns
by Gabriela Figas, Anna Hadamus, Michalina Błażkiewicz and Jolanta Kujawa
Sensors 2023, 23(8), 4170; https://0-doi-org.brum.beds.ac.uk/10.3390/s23084170 - 21 Apr 2023
Viewed by 1933
Abstract
The activity of muscles during motion in one direction should be symmetrical when compared to the activity of the contralateral muscles during motion in the opposite direction, while symmetrical movements should result in symmetrical muscle activation. The literature lacks data on the symmetry [...] Read more.
The activity of muscles during motion in one direction should be symmetrical when compared to the activity of the contralateral muscles during motion in the opposite direction, while symmetrical movements should result in symmetrical muscle activation. The literature lacks data on the symmetry of neck muscle activation. Therefore, this study aimed to analyse the activity of the upper trapezius (UT) and sternocleidomastoid (SCM) muscles at rest and during basic motions of the neck and to determine the symmetry of the muscle activation. Surface electromyography (sEMG) was collected from UT and SCM bilaterally during rest, maximum voluntary contraction (MVC) and six functional movements from 18 participants. The muscle activity was related to the MVC, and the Symmetry Index was calculated. The muscle activity at rest was 23.74% and 27.88% higher on the left side than on the right side for the UT and SCM, respectively. The highest asymmetries during motion were for the SCM for the right arc movement (116%) and for the UT in the lower arc movement (55%). The lowest asymmetry was recorded for extension–flexion movement for both muscles. It was concluded that this movement can be useful for assessing the symmetry of neck muscles’ activation. Further studies are required to verify the above-presented results, determine muscle activation patterns and compare healthy people to patients with neck pain. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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11 pages, 1541 KiB  
Article
Effects of Law Enforcement Load Carriage Systems on Muscle Activity and Coordination during Walking: An Exploratory Study
by Joel Martin, Megan Sax van der Weyden and Marcie Fyock-Martin
Sensors 2023, 23(8), 4052; https://0-doi-org.brum.beds.ac.uk/10.3390/s23084052 - 17 Apr 2023
Viewed by 1136
Abstract
Law enforcement officers (LEOs) commonly wear a duty belt (DB) or tactical vest (TV) and from prior findings, these forms of load carriage (LC) likely alter muscular activity. However, studies on the effects of LEO LC on muscular activity and coordination are limited [...] Read more.
Law enforcement officers (LEOs) commonly wear a duty belt (DB) or tactical vest (TV) and from prior findings, these forms of load carriage (LC) likely alter muscular activity. However, studies on the effects of LEO LC on muscular activity and coordination are limited in the current literature. The present study examined the effects of LEO load carriage on muscular activity and coordination. Twenty-four volunteers participated in the study (male = 13, age = 24.5 ± 6.0 years). Surface electromyography (sEMG) sensors were placed on the vastus lateralis, biceps femoris, multifidus, and lower rectus abdominus. Participants completed treadmill walking for two load carriage conditions (duty belt and tactical vest) and a control condition. Mean activity, sample entropy and Pearson correlation coefficients were computed for each muscle pair during the trials. The duty belt and tactical vest resulted in an increase in muscle activity in several muscles; however, no differences between the duty belt and tactical vest were found. Consistently across the conditions, the largest correlations were observed between the left and right multifidus (r = 0.33–0.68) and rectus abdominus muscles (0.34–0.55). There were statistically small effects (p < 0.05, η2 = 0.031 to 0.076) of the LC on intermuscular coordination. No effect (p > 0.05) of the LC on sample entropy was found for any muscle. The findings indicate that LEO LC causes small differences in muscular activity and coordination during walking. Future research should incorporate heavier loads and longer durations. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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23 pages, 1475 KiB  
Article
A Preliminary Study of the Efficacy of Using a Wrist-Worn Multiparameter Sensor for the Prediction of Cognitive Flow States in University-Level Students
by Josephine Graft, William Romine, Brooklynn Watts, Noah Schroeder, Tawsik Jawad and Tanvi Banerjee
Sensors 2023, 23(8), 3957; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083957 - 13 Apr 2023
Viewed by 1852
Abstract
Engagement is enhanced by the ability to access the state of flow during a task, which is described as a full immersion experience. We report two studies on the efficacy of using physiological data collected from a wearable sensor for the automated prediction [...] Read more.
Engagement is enhanced by the ability to access the state of flow during a task, which is described as a full immersion experience. We report two studies on the efficacy of using physiological data collected from a wearable sensor for the automated prediction of flow. Study 1 took a two-level block design where activities were nested within its participants. A total of five participants were asked to complete 12 tasks that aligned with their interests while wearing the Empatica E4 sensor. This yielded 60 total tasks across the five participants. In a second study representing daily use of the device, a participant wore the device over the course of 10 unstructured activities over 2 weeks. The efficacy of the features derived from the first study were tested on these data. For the first study, a two-level fixed effects stepwise logistic regression procedure indicated that five features were significant predictors of flow. In total, two were related to skin temperature (median change with respect to the baseline and skewness of the temperature distribution) and three were related to acceleration (the acceleration skewness in the x and y directions and the kurtosis of acceleration in the y direction). Logistic regression and naïve Bayes models provided a strong classification performance (AUC > 0.7, between-participant cross-validation). For the second study, these same features yielded a satisfactory prediction of flow for the new participant wearing the device in an unstructured daily use setting (AUC > 0.7, leave-one-out cross-validation). The features related to acceleration and skin temperature appear to translate well for the tracking of flow in a daily use environment. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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20 pages, 4682 KiB  
Article
Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System
by Rachele Rossanigo, Marco Caruso, Stefano Bertuletti, Franca Deriu, Marco Knaflitz, Ugo Della Croce and Andrea Cereatti
Sensors 2023, 23(8), 3921; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083921 - 12 Apr 2023
Cited by 1 | Viewed by 2511
Abstract
The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground [...] Read more.
The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10–46 mm, 14–18 mm, and 39–52 cm2, respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 1448 KiB  
Article
Assessment of Passive Upper Limb Stiffness and Its Function in Post-Stroke Individuals Wearing an Inertial Sensor during the Pendulum Test
by Milene Soares Nogueira de Lima, Clarissa Cardoso dos Santos Couto Paz, Thais Gontijo Ribeiro and Emerson Fachin-Martins
Sensors 2023, 23(7), 3487; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073487 - 27 Mar 2023
Cited by 1 | Viewed by 1404
Abstract
This article proposes the evaluation of the passive movement of the affected elbow during the pendulum test in people with stroke and its correlation with the main clinical scales (Modified Ashworth Scale, Motor Activity Log, and Fulg Meyer). An inertial sensor was attached [...] Read more.
This article proposes the evaluation of the passive movement of the affected elbow during the pendulum test in people with stroke and its correlation with the main clinical scales (Modified Ashworth Scale, Motor Activity Log, and Fulg Meyer). An inertial sensor was attached to the forearm of seven subjects, who then passively flexed and extended the elbow. Joint angles and variables that indicate viscoelastic properties, stiffness (K), damping (B), E1 amp, F1 amp, and relaxation indices were collected. The results show that the FM scale is significantly correlated with the natural frequency (p = 0.024). The MAL amount-of-use score correlates with the natural frequency (p = 0.024). The variables E1 amp, F1 amp, RI, and ERI are not correlated with the clinical scales, but they correlate with each other; the variable E1 amp correlates with F1 amp (p = 0.024) and RI (p = 0.024), while F1 amp correlates with ERI (p = 0.024). There was also a correlation between the natural frequency and K (r = 0.96, p = 0.003). Non-linear results were found for the properties of the elbow joint during the pendulum test, which may be due to the presence of neural and non-neural factors. These results may serve as a reference for future studies if alternative scales do not provide an accurate reflection. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 2194 KiB  
Article
Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study
by Vrutangkumar V. Shah, Barbara H. Brumbach, Sean Pearson, Paul Vasilyev, Edward King, Patricia Carlson-Kuhta, Martina Mancini, Fay B. Horak, Kristen Sowalsky, James McNames and Mahmoud El-Gohary
Sensors 2023, 23(4), 2296; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042296 - 18 Feb 2023
Cited by 4 | Viewed by 2113
Abstract
Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for [...] Read more.
Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland–Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test–retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland–Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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12 pages, 4464 KiB  
Article
Actuation Strategies for a Wearable Cable-Driven Exosuit Based on Synergies in Younger and Older Adults
by Javier Bermejo-García, Daniel Rodríguez Jorge, Francisco Romero-Sánchez, Ashwin Jayakumar and Francisco J. Alonso-Sánchez
Sensors 2023, 23(1), 261; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010261 - 27 Dec 2022
Cited by 4 | Viewed by 1645
Abstract
Older adults (aged 55 years and above) have greater difficulty carrying out activities of daily living than younger adults (aged 25–55 years). Although age-related changes in human gait kinetics are well documented in qualitative terms in the scientific literature, these differences may be [...] Read more.
Older adults (aged 55 years and above) have greater difficulty carrying out activities of daily living than younger adults (aged 25–55 years). Although age-related changes in human gait kinetics are well documented in qualitative terms in the scientific literature, these differences may be quantified and analyzed using the analysis of motor control strategies through kinetic synergies. The gaits of two groups of people (older and younger adults), each with ten members, were analyzed on a treadmill at a constant controlled speed and their gait kinetics were recorded. The decomposition of the kinetics into synergies was applied to the joint torques at the hip, knee, and ankle joints. Principal components determined the similarity of the kinetic torques in the three joints analyzed and the effect of the walking speed on the coordination pattern. A total of three principal components were required to describe enough information with minimal loss. The results suggest that the older group showed a change in coordination strategy compared to that of the younger group. The main changes were related to the ankle and hip torques, both showing significant differences (p-value <0.05) between the two groups. The findings suggest that the differences between the gait patterns of the two groups were closely related to a reduction in ankle torque and an increase in hip torque. This change in gait pattern may affect the rehabilitation strategy used when designing general-purpose rehabilitation devices or rehabilitation/training programs for the elderly. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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19 pages, 4642 KiB  
Article
Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor
by Hui R. Ng, Isidore Sossa, Yunwoo Nam and Jong-Hoon Youn
Sensors 2023, 23(1), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010193 - 24 Dec 2022
Cited by 4 | Viewed by 1974
Abstract
The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are [...] Read more.
The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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15 pages, 1791 KiB  
Article
Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia
by Jennifer Freytag, Ram Kinker Mishra, Richard L. Street, Jr., Angela Catic, Lilian Dindo, Lea Kiefer, Bijan Najafi and Aanand D. Naik
Sensors 2022, 22(24), 9923; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249923 - 16 Dec 2022
Cited by 4 | Viewed by 1782
Abstract
Aligning treatment with patients’ self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using [...] Read more.
Aligning treatment with patients’ self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4–6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient’s priorities. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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12 pages, 1963 KiB  
Article
Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor
by Zitong Wang, Keren Zhu, Archana Kaur, Robyn Recker, Jingzhen Yang and Asimina Kiourti
Sensors 2022, 22(23), 9115; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239115 - 24 Nov 2022
Cited by 1 | Viewed by 2298
Abstract
Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive [...] Read more.
Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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16 pages, 1275 KiB  
Article
Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension
by Sung-Nien Yu, I-Mei Lin, San-Yu Wang, Yi-Cheng Hou, Sheng-Po Yao, Chun-Ying Lee, Chai-Jan Chang, Chih-Sheng Chu and Tsung-Hsien Lin
Sensors 2022, 22(22), 8771; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228771 - 13 Nov 2022
Viewed by 1254
Abstract
Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with [...] Read more.
Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 1112 KiB  
Article
Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors
by Reza Mohammadi-Ghazi, Hung Nguyen, Ram Kinker Mishra, Ana Enriquez, Bijan Najafi, Christopher D. Stephen, Anoopum S. Gupta, Jeremy D. Schmahmann and Ashkan Vaziri
Sensors 2022, 22(20), 7993; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207993 - 20 Oct 2022
Cited by 4 | Viewed by 1659
Abstract
The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we [...] Read more.
The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants’ assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 415 KiB  
Article
Modulating Heart Rate Variability through Deep Breathing Exercises and Transcutaneous Auricular Vagus Nerve Stimulation: A Study in Healthy Participants and in Patients with Rheumatoid Arthritis or Systemic Lupus Erythematosus
by Mette Kjeldsgaard Jensen, Sally Søgaard Andersen, Stine Søgaard Andersen, Caroline Hundborg Liboriussen, Salome Kristensen and Mads Jochumsen
Sensors 2022, 22(20), 7884; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207884 - 17 Oct 2022
Cited by 3 | Viewed by 2891
Abstract
Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are associated with an impaired autonomic nervous system and vagus nerve function. Electrical or physiological (deep breathing—DB) vagus nerve stimulation (VNS) could be a potential treatment approach, but no direct comparison has been made. In [...] Read more.
Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are associated with an impaired autonomic nervous system and vagus nerve function. Electrical or physiological (deep breathing—DB) vagus nerve stimulation (VNS) could be a potential treatment approach, but no direct comparison has been made. In this study, the effect of transcutaneous auricular VNS (taVNS) and DB on vagal tone was compared in healthy participants and RA or SLE patients. The vagal tone was estimated using time-domain heart-rate variability (HRV) parameters. Forty-two healthy participants and 52 patients performed 30 min of DB and 30 min of taVNS on separate days. HRV was recorded before and immediately after each intervention. For the healthy participants, all HRV parameters increased after DB (SDNN + RMSSD: 21–46%), while one HRV parameter increased after taVNS (SDNN: 16%). For the patients, all HRV parameters increased after both DB (17–31%) and taVNS (18–25%), with no differences between the two types of VNS. DB was associated with the largest elevation of the HRV parameters in healthy participants, while both types of VNS led to elevated HRV parameters in the patients. The findings support a potential use of VNS as a new treatment approach, but the clinical effects need to be investigated in future studies. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 298 KiB  
Article
Investigating the Dose-Response Relationship between Deep Breathing and Heart Rate Variability in Healthy Participants and Across-Days Reliability in Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus
by Caroline Hundborg Liboriussen, Stine Søgaard Andersen, Sally Søgaard Andersen, Mette Kjeldsgaard Jensen, Mads Jochumsen and Salome Kristensen
Sensors 2022, 22(18), 6849; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186849 - 10 Sep 2022
Cited by 1 | Viewed by 1797
Abstract
Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are associated with autonomic dysfunction, potentially through reduced vagus nerve tone. Vagus nerve stimulation has been proposed as an anti-inflammatory treatment, and it can be performed through deep breathing (DB) exercises. In this study, the [...] Read more.
Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are associated with autonomic dysfunction, potentially through reduced vagus nerve tone. Vagus nerve stimulation has been proposed as an anti-inflammatory treatment, and it can be performed through deep breathing (DB) exercises. In this study, the dose-response relationship between DB exercises and heart rate variability (HRV) was investigated in healthy participants and reliability across days in patients with RA and SLE. On three separate days, 41 healthy participants performed DB for: 5, 15, or 30 min. On two separate days, 52 RA or SLE patients performed DB with the dose associated with the highest HRV increase in healthy participants. The HRV was estimated from ECG-recordings recorded prior and post the DB exercises. Increases in dose led to larger HRV-responses. Thirty minutes led to the largest HRV-response. In the RA and SLE patients, this dose increased the HRV-parameters consistently across the two days, indicating reliability. DB increases HRV in healthy participants and RA or SLE patients, which indicates stimulation of the vagus nerve. Of the tested durations, 30 min of DB was the optimal period of stimulation. A potential anti-inflammatory effect of DB exercises should be investigated in future studies. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 4048 KiB  
Article
Smart-Home Concept for Remote Monitoring of Instrumental Activities of Daily Living (IADL) in Older Adults with Cognitive Impairment: A Proof of Concept and Feasibility Study
by Myeounggon Lee, Ram Kinker Mishra, Anmol Momin, Nesreen El-Refaei, Amir Behzad Bagheri, Michele K. York, Mark E. Kunik, Marc Derhammer, Borna Fatehi, James Lim, Rylee Cole, Gregory Barchard, Ashkan Vaziri and Bijan Najafi
Sensors 2022, 22(18), 6745; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186745 - 07 Sep 2022
Cited by 13 | Viewed by 2999
Abstract
Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of [...] Read more.
Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of three major components: (1) wireless physical tags (pTAG) attached to objects of interest, (2) a pendant–sensor to monitor physical activities and detect interaction with pTAGs, and (3) an interactive tablet as a gateway to transfer data to a secured cloud. Four studies, including an exploratory clinical study with five older adults with clinically confirmed cognitive impairment, who used IADLSys for 24 h/7 days, were performed to confirm IADLSys feasibility, acceptability, adherence, and validity of detecting IADLs of interest and physical activity. Exploratory tests in two cases with severe and mild cognitive impairment, respectively, revealed that a case with severe cognitive impairment either overestimated or underestimated the frequency of performed IADLs, whereas self-reporting and objective IADL were comparable for the case with mild cognitive impairment. This feasibility and acceptability study may pave the way to implement the smart-home concept to remotely monitor IADL, which in turn may assist in providing personalized support to people with cognitive impairment, while tracking the decline in both physical and cognitive function. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 687 KiB  
Article
Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods
by Hansel Hu, Tin Lai and Farnaz Farid
Sensors 2022, 22(16), 6155; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166155 - 17 Aug 2022
Cited by 4 | Viewed by 1754
Abstract
Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based [...] Read more.
Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based predictive model to detect adolescent diabetes. The model applies supervised machine learning and a novel feature selection method to the National Health and Nutritional Examination Survey datasets after an exhaustive search to select reliable and accurate data. The best model achieved an area under the curve (AUC) score of 71%. This research proves that a screening tool based on supervised machine learning models can assist in the automated detection of youth diabetes. It also identifies some critical predictors to such detection using Lasso Regression, Random Forest Importance and Gradient Boosted Tree Importance feature selection methods. The most contributing features to Youth diabetes detection are physical characteristics (e.g., waist, leg length, gender), dietary information (e.g., water, protein, sodium) and demographics. These predictors can be further utilised in other areas of medical research, such as electronic medical history. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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35 pages, 1859 KiB  
Article
Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry
by Radhya Sahal, Saeed H. Alsamhi and Kenneth N. Brown
Sensors 2022, 22(15), 5918; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155918 - 08 Aug 2022
Cited by 33 | Viewed by 7810
Abstract
Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type [...] Read more.
Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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15 pages, 3300 KiB  
Article
Pilot Study for Correlation of Heart Rate Variability and Dopamine Transporter Brain Imaging in Patients with Parkinsonian Syndrome
by Devdutta S. Warhadpande, Jiayan Huo, William A. Libling, Carol Stuehm, Bijan Najafi, Scott Sherman, Hong Lei, Janet Meiling Roveda and Phillip H. Kuo
Sensors 2022, 22(13), 5055; https://0-doi-org.brum.beds.ac.uk/10.3390/s22135055 - 05 Jul 2022
Cited by 1 | Viewed by 2493
Abstract
Background: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the [...] Read more.
Background: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the substantia nigra and, thus, dysfunction of the nigrostriatal pathway. In addition to the motor symptoms of bradykinesia, rigidity, tremors, and postural instability, nonmotor symptoms such as autonomic dysregulation (AutD) can also occur. Heart rate variability (HRV) has been used as a measure of AutD and has shown to be prognostic in diseases such as diabetes mellitus and cirrhosis, as well as PD. I-123 ioflupane, a gamma ray-emitting radiopharmaceutical used in single-photon emission computed tomography (SPECT), is used to measure the loss of dopaminergic neurons in PD. Through the combination of SPECT and HRV, we tested the hypothesis that asymmetrically worse left-sided neuronal loss would cause greater AutD. Methods: 51 patients were enrolled on the day of their standard of care I-123 ioflupane scan for the work-up of possible Parkinsonian syndrome. Demographic information, medical and medication history, and ECG data were collected. HRV metrics were extracted from the ECG data. I-123 ioflupane scans were interpreted by a board-certified nuclear radiologist and quantified by automated software to generate striatal binding ratios (SBRs). Statistical analyses were performed to find correlations between the HRV and SPECT parameters. Results: 32 patients were excluded from the final analysis because of normal scans, prior strokes, cardiac disorders and procedures, or cancer. Abnormal I-123 ioflupane scans were clustered using T-SNE, and one-way ANOVA was performed to compare HRV and SBR parameters. The analysis was repeated after the exclusion of patients taking angiotensin-converting enzyme inhibitors, given the known mechanism on autonomic function. Subsequent analysis showed a significant difference between the high-frequency domains of heart rate variability, asymmetry of the caudate SBR, and putamen-to-caudate SBR. Conclusion: Our results support the hypothesis that more imbalanced (specifically worse left-sided) neuronal loss results in greater AutD. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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24 pages, 815 KiB  
Article
Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd
by Mohamed Elshafei, Diego Elias Costa and Emad Shihab
Sensors 2022, 22(4), 1454; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041454 - 14 Feb 2022
Cited by 6 | Viewed by 2371
Abstract
Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have [...] Read more.
Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20–46 and 24–46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject’s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects’ data consumption. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 804 KiB  
Article
The Validity of Wireless Earbud-Type Wearable Sensors for Head Angle Estimation and the Relationships of Head with Trunk, Pelvis, Hip, and Knee during Workouts
by Ae-Ryeong Kim, Ju-Hyun Park, Si-Hyun Kim, Kwang Bok Kim and Kyue-Nam Park
Sensors 2022, 22(2), 597; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020597 - 13 Jan 2022
Cited by 4 | Viewed by 2547
Abstract
The present study was performed to investigate the validity of a wireless earbud-type inertial measurement unit (Ear-IMU) sensor used to estimate head angle during four workouts. In addition, relationships between head angle obtained from the Ear-IMU sensor and the angles of other joints [...] Read more.
The present study was performed to investigate the validity of a wireless earbud-type inertial measurement unit (Ear-IMU) sensor used to estimate head angle during four workouts. In addition, relationships between head angle obtained from the Ear-IMU sensor and the angles of other joints determined with a 3D motion analysis system were investigated. The study population consisted of 20 active volunteers. The Ear-IMU sensor measured the head angle, while a 3D motion analysis system simultaneously measured the angles of the head, trunk, pelvis, hips, and knees during workouts. Comparison with the head angle measured using the 3D motion analysis system indicated that the validity of the Ear-IMU sensor was very strong or moderate in the sagittal and frontal planes. In addition, the trunk angle in the frontal plane showed a fair correlation with the head angle determined with the Ear-IMU sensor during a single-leg squat, reverse lunge, and standing hip abduction; the correlation was poor in the sagittal plane. Our results indicated that the Ear-IMU sensor can be used to directly estimate head motion and indirectly estimate trunk motion. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 2287 KiB  
Article
Digital Biomarkers for the Objective Assessment of Disability in Neurogenic Thoracic Outlet Syndrome
by Bijan Najafi, Mohsen Zahiri, Changhong Wang, Anmol Momin, Paul Paily and Bryan M. Burt
Sensors 2021, 21(22), 7462; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227462 - 10 Nov 2021
Cited by 1 | Viewed by 2371
Abstract
Neurogenic thoracic outlet syndrome (nTOS) is a musculoskeletal disorder in which compression of the brachial plexus between the scalene muscles of the neck and the first rib results in disabling upper extremity pain and paresthesia. Currently there are no objective metrics for assessing [...] Read more.
Neurogenic thoracic outlet syndrome (nTOS) is a musculoskeletal disorder in which compression of the brachial plexus between the scalene muscles of the neck and the first rib results in disabling upper extremity pain and paresthesia. Currently there are no objective metrics for assessing the disability of nTOS or for monitoring response to its therapy. We aimed to develop digital biomarkers of upper extremity motor capacity that could objectively measure the disability of nTOS using an upper arm inertial sensor and a 20-s upper extremity task that provokes nTOS symptoms. We found that digital biomarkers of slowness, power, and rigidity statistically differentiated the affected extremities of patients with nTOS from their contralateral extremities (n = 16) and from the extremities of healthy controls (n = 13); speed and power had the highest effect sizes. Digital biomarkers representing slowness, power, and rigidity correlated with patient-reported outcomes collected with the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and the visual analog scale of pain (VAS); speed had the highest correlation. Digital biomarkers of exhaustion correlated with failure of physical therapy in treating nTOS; and digital biomarkers of slowness, power, and exhaustion correlated with favorable response to nTOS surgery. In conclusion, sensor-derived digital biomarkers can objectively assess the impairment of motor capacity resultant from nTOS, and correlate with patient-reported symptoms and response to therapy. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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17 pages, 2383 KiB  
Article
Wearable Electronic Tongue for Non-Invasive Assessment of Human Sweat
by Magnus Falk, Emelie J. Nilsson, Stefan Cirovic, Bogdan Tudosoiu and Sergey Shleev
Sensors 2021, 21(21), 7311; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217311 - 03 Nov 2021
Cited by 4 | Viewed by 2016
Abstract
Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating [...] Read more.
Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating metal electrodes on a flexible circuit board and used to non-invasively monitor sweat on the body. The data obtained from the measurements were treated by multivariate data processing. Using principal component analysis to analyze the data collected by the wearable electronic tongue enabled differentiation of sweat samples of different chemical composition, and when combined with 1H-NMR sample differentiation could be attributed to changing analyte concentrations. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 1209 KiB  
Article
Wearable Sensors for Measurement of Viewing Behavior, Light Exposure, and Sleep
by Khob R. Bhandari, Hanieh Mirhajianmoghadam and Lisa A. Ostrin
Sensors 2021, 21(21), 7096; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217096 - 26 Oct 2021
Cited by 10 | Viewed by 3088
Abstract
The purpose of this study was to compare two wearable sensors to each other and to a questionnaire in an adult population. For one week, participants aged 29.2 ± 5.5 years (n = 25) simultaneously wore a Clouclip, a spectacle-mounted device that [...] Read more.
The purpose of this study was to compare two wearable sensors to each other and to a questionnaire in an adult population. For one week, participants aged 29.2 ± 5.5 years (n = 25) simultaneously wore a Clouclip, a spectacle-mounted device that records viewing distance and illuminance, and an Actiwatch, a wrist-worn device that measures illuminance and activity. Participants maintained a daily log of activities and completed an activity questionnaire. Objective measures of time outdoors, near (10–< 60 cm) and intermediate (60–100 cm) viewing, and sleep duration were assessed with respect to the daily log and questionnaire. Findings showed that time outdoors per day from the questionnaire (3.2 ± 0.3 h) was significantly greater than the Clouclip (0.9 ± 0.8 h) and Actiwatch (0.7 ± 0.1 h, p < 0.001 for both). Illuminance from the Actiwatch was systematically lower than the Clouclip. Daily near viewing duration was similar between the questionnaire (5.7 ± 0.6 h) and Clouclip (6.1 ± 0.4 h, p = 0.76), while duration of intermediate viewing was significantly different between methods (p < 0.001). In conclusion, self-reported time outdoors and viewing behaviors were different than objective measures. The Actiwatch and Clouclip are valuable tools for studying temporal patterns of behavioral factors such as near work, light exposure, and sleep. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 4778 KiB  
Article
Characterization and Comparison of Biodegradable Printed Capacitive Humidity Sensors
by Emma Wawrzynek, Carol Baumbauer and Ana Claudia Arias
Sensors 2021, 21(19), 6557; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196557 - 30 Sep 2021
Cited by 21 | Viewed by 3789
Abstract
Flexible and biodegradable sensors are advantageous for their versatility in a range of areas from smart packaging to agriculture. In this work, we characterize and compare the performance of interdigitated electrode (IDE) humidity sensors printed on different biodegradable substrates. In these IDE capacitive [...] Read more.
Flexible and biodegradable sensors are advantageous for their versatility in a range of areas from smart packaging to agriculture. In this work, we characterize and compare the performance of interdigitated electrode (IDE) humidity sensors printed on different biodegradable substrates. In these IDE capacitive devices, the substrate acts as the sensing layer. The dielectric constant of the substrate increases as the material absorbs water from the atmosphere. Consequently, the capacitance across the electrodes is a function of environmental relative humidity. Here, the performance of polylactide (PLA), glossy paper, and potato starch as a sensing layer is compared to that of nonbiodegradable polyethylene terephthalate (PET). The capacitance across inkjet-printed silver electrodes is measured in environmental conditions ranging from 15 to 90% relative humidity. The sensitivity, response time, hysteresis, and temperature dependency are compared for the sensors. The relationship between humidity and capacitance across the sensors can be modeled by exponential growth with an R2 value of 0.99, with paper and starch sensors having the highest overall sensitivity. The PET and PLA sensors have response and recovery times under 5 min and limited hysteresis. However, the paper and starch sensors have response and recovery times closer to 20 min, with significant hysteresis around 100%. The PET and starch sensors are temperature independent, while the PLA and paper sensors display thermal drift that increases with temperature. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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14 pages, 22766 KiB  
Article
Differential Soft Sensor-Based Measurement of Interactive Force and Assistive Torque for a Robotic Hip Exoskeleton
by Sun’an Wang, Binquan Zhang, Zhenyuan Yu and Yu’ang Yan
Sensors 2021, 21(19), 6545; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196545 - 30 Sep 2021
Cited by 12 | Viewed by 2859
Abstract
With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable [...] Read more.
With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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12 pages, 1183 KiB  
Article
Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning
by Catherine Park, Ramkinker Mishra, Jonathan Golledge and Bijan Najafi
Sensors 2021, 21(16), 5289; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165289 - 05 Aug 2021
Cited by 21 | Viewed by 3829
Abstract
Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty [...] Read more.
Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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19 pages, 4242 KiB  
Article
Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes
by Jesús Berián, Ignacio Bravo, Alfredo Gardel-Vicente, José-Luis Lázaro-Galilea and Mercedes Rigla
Sensors 2021, 21(15), 5226; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155226 - 02 Aug 2021
Cited by 1 | Viewed by 2675
Abstract
Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several [...] Read more.
Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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13 pages, 11305 KiB  
Communication
Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
by Wen-Lan Wu, Jing-Min Liang, Chien-Fei Chen, Kuei-Lan Tsai, Nian-Shing Chen, Kuo-Chin Lin and Ing-Jer Huang
Sensors 2021, 21(11), 3870; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113870 - 03 Jun 2021
Cited by 6 | Viewed by 4200
Abstract
Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male [...] Read more.
Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. Results: The overall model’s recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills “explosive force of the forearm” and “wrist muscle control” were extracted, and their factor scores were standardized (0–100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. Conclusion: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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21 pages, 5601 KiB  
Article
An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
by Jiaen Wu, Kiran Kuruvithadam, Alessandro Schaer, Richie Stoneham, George Chatzipirpiridis, Chris Awai Easthope, Gill Barry, James Martin, Salvador Pané, Bradley J. Nelson, Olgaç Ergeneman and Hamdi Torun
Sensors 2021, 21(8), 2869; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082869 - 19 Apr 2021
Cited by 14 | Viewed by 5979
Abstract
The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis [...] Read more.
The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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Review

Jump to: Research, Other

16 pages, 1708 KiB  
Review
Digital Biomarkers of Gait and Balance in Diabetic Foot, Measurable by Wearable Inertial Measurement Units: A Mini Review
by Gu Eon Kang, Angeloh Stout, Ke’Vaughn Waldon, Seungmin Kang, Amanda L. Killeen, Peter A. Crisologo, Michael Siah, Daniel Jupiter, Bijan Najafi, Ashkan Vaziri and Lawrence A. Lavery
Sensors 2022, 22(23), 9278; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239278 - 29 Nov 2022
Cited by 2 | Viewed by 2261
Abstract
People with diabetic foot frequently exhibit gait and balance dysfunction. Recent advances in wearable inertial measurement units (IMUs) enable to assess some of the gait and balance dysfunction associated with diabetic foot (i.e., digital biomarkers of gait and balance). However, there is no [...] Read more.
People with diabetic foot frequently exhibit gait and balance dysfunction. Recent advances in wearable inertial measurement units (IMUs) enable to assess some of the gait and balance dysfunction associated with diabetic foot (i.e., digital biomarkers of gait and balance). However, there is no review to inform digital biomarkers of gait and balance dysfunction related to diabetic foot, measurable by wearable IMUs (e.g., what gait and balance parameters can wearable IMUs collect? Are the measurements repeatable?). Accordingly, we conducted a web-based, mini review using PubMed. Our search was limited to human subjects and English-written papers published in peer-reviewed journals. We identified 20 papers in this mini review. We found preliminary evidence of digital biomarkers of gait and balance dysfunction in people with diabetic foot, such as slow gait speed, large gait variability, unstable gait initiation, and large body sway. However, due to heterogeneities in included papers in terms of study design, movement tasks, and small sample size, more studies are recommended to confirm this preliminary evidence. Additionally, based on our mini review, we recommend establishing appropriate strategies to successfully incorporate wearable-based assessment into clinical practice for diabetic foot care. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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17 pages, 372 KiB  
Review
Lower Limb Exoskeleton Sensors: State-of-the-Art
by Slávka Neťuková, Martin Bejtic, Christiane Malá, Lucie Horáková, Patrik Kutílek, Jan Kauler and Radim Krupička
Sensors 2022, 22(23), 9091; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239091 - 23 Nov 2022
Cited by 7 | Viewed by 2818
Abstract
Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within [...] Read more.
Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human–machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
33 pages, 2890 KiB  
Review
A Systematic Review of Commercial Smart Gloves: Current Status and Applications
by Manuel Caeiro-Rodríguez, Iván Otero-González, Fernando A. Mikic-Fonte and Martín Llamas-Nistal
Sensors 2021, 21(8), 2667; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082667 - 10 Apr 2021
Cited by 74 | Viewed by 13803
Abstract
Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent [...] Read more.
Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015–2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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Other

Jump to: Research, Review

16 pages, 1757 KiB  
Systematic Review
Network Meta-Analysis of Trials Testing If Home Exercise Programs Informed by Wearables Measuring Activity Improve Peripheral Artery Disease Related Walking Impairment
by Shivshankar Thanigaimani, Harry Jin, Munasinghe Tharindu Silva and Jonathan Golledge
Sensors 2022, 22(20), 8070; https://0-doi-org.brum.beds.ac.uk/10.3390/s22208070 - 21 Oct 2022
Cited by 1 | Viewed by 1921
Abstract
Background: This study aimed to investigate whether home exercise programs informed by wearable activity monitors improved walking ability of patients with peripheral artery disease (PAD). Methods: A systematic literature search was performed to identify randomised controlled trials (RCT) testing home exercise that were [...] Read more.
Background: This study aimed to investigate whether home exercise programs informed by wearable activity monitors improved walking ability of patients with peripheral artery disease (PAD). Methods: A systematic literature search was performed to identify randomised controlled trials (RCT) testing home exercise that were or were not informed by wearable activity monitors. The primary outcome was the change in walking distance measured by a six-minute walking test or treadmill test over the course of the trial. Network meta-analysis (NMA) was performed using the gemtc R statistical package. The risk of bias was assessed using Cochrane tool for assessing risk of bias in RCTs (RoB 2.0). Results: A total of 14 RCTs involving 1544 participants were included. Nine trials used wearable activity monitors to inform the home exercise program tested, while five trials did not use wearable activity monitors to inform the home exercise program tested. Overall quality assessment showed 12 trials to be at low risk of bias and two trials at high risk of bias. Home exercise programs informed by wearable activity monitors significantly improved walking distance compared to non-exercise controls (Mean difference, MD: 32.8 m [95% credible interval, CrI: 6.1, 71.0]) but not compared to home exercise programs not informed by wearable activity monitors (MD: 4.7 m [95% CrI: −38.5, 55.4]). Conclusions: Home exercise informed by wearable activity monitors improve walking ability of patients with PAD. It is, however, unclear if activity monitoring informed exercise programs are more effective than exercise programs not using activity monitors. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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11 pages, 248 KiB  
Perspective
Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market
by Jaime K. Devine, Lindsay P. Schwartz and Steven R. Hursh
Sensors 2022, 22(7), 2731; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072731 - 02 Apr 2022
Cited by 9 | Viewed by 2123
Abstract
Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting [...] Read more.
Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
12 pages, 374 KiB  
Systematic Review
The Effect of Implanted Functional Electrical Stimulation on Gait Performance in Stroke Survivors: A Systematic Review
by Gu Eon Kang, Rebecca Frederick, Brandon Nunley, Lawrence Lavery, Yasin Dhaher, Bijan Najafi and Stuart Cogan
Sensors 2021, 21(24), 8323; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248323 - 13 Dec 2021
Cited by 5 | Viewed by 3154
Abstract
The emerging literature suggests that implantable functional electrical stimulation may improve gait performance in stroke survivors. However, there is no review providing the possible therapeutic effects of implanted functional electrical stimulation on gait performance in stroke survivors. We performed a web-based, systematic paper [...] Read more.
The emerging literature suggests that implantable functional electrical stimulation may improve gait performance in stroke survivors. However, there is no review providing the possible therapeutic effects of implanted functional electrical stimulation on gait performance in stroke survivors. We performed a web-based, systematic paper search using PubMed, the Cochrane Library, and EMBASE. We limited the search results to human subjects and papers published in peer-reviewed journals in English. We did not restrict demographic or clinical characteristics. We included 10 papers in the current systematic review. Across all included studies, we found preliminary evidence of the potential therapeutic effects of functional electrical stimulation on walking endurance, walking speed, ankle mobility, and push-off force in stroke survivors. However, due to the heterogeneity between the included studies, small sample size, and lack of randomized controlled trials, more studies are critically needed to confirm whether implanted functional electrical stimulation can improve gait performance in stroke survivors. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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