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Feature Papers in Wearables Section 2021
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Feature Papers in Wearables Section 2021

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 91953

Special Issue Editor

Dr. Roozbeh Ghaffari

grade E-Mail Website
Guest Editor
Querrey Simpson Institute for Bioelectronics, Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA
Interests: flexible electronics; biosensors; wearable computing; MEMS; neuroscience; microfluidics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Wearables section is now compiling a collection of papers submitted exclusively by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. The Special Issue engages in topics such as emerging wearable systems with integrated sensors (motion, ECG, HRV, GSR, blood pressure, biochemical sensors, and others), actuators (drug delivery, electrical stimulus, thermal actuator, phototherapy) and data analytics engines for addressing key chronic medical conditions, diseases, health diagnostics, stress (mental and physical), wellness, and fitness applications.

The purpose of this Special Issue is to publish a set of papers that typifies the very best insightful and influential original articles or review where our section’s EBMs and outstanding scholars discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted.

Taking this opportunity, we would also like to call on more excellent scholars to join the Wearables section so we can achieve more milestones together.

Prof. Dr. Roozbeh Ghaffari
Editor-in-Chief

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.

Published Papers (21 papers)

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28 pages, 9288 KiB  
Article
Design of a Planner-Based Intervention to Facilitate Diet Behaviour Change in Type 2 Diabetes
by Kevin A. Cradock, Leo R. Quinlan, Francis M. Finucane, Heather L. Gainforth, Kathleen A. Martin Ginis, Elizabeth B.-N. Sanders and Gearóid ÓLaighin
Sensors 2022, 22(7), 2795; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072795 - 06 Apr 2022
Cited by 4 | Viewed by 3785
Abstract
Diet behaviour is influenced by the interplay of the physical and social environment as well as macro-level and individual factors. In this study, we focus on diet behaviour at an individual level and describe the design of a behaviour change artefact to support [...] Read more.
Diet behaviour is influenced by the interplay of the physical and social environment as well as macro-level and individual factors. In this study, we focus on diet behaviour at an individual level and describe the design of a behaviour change artefact to support diet behaviour change in persons with type 2 diabetes. This artefact was designed using a human-centred design methodology and the Behaviour Change Wheel framework. The designed artefact sought to support diet behaviour change through the addition of healthy foods and the reduction or removal of unhealthy foods over a 12-week period. These targeted behaviours were supported by the enabling behaviours of water consumption and mindfulness practice. The artefact created was a behaviour change planner in calendar format, that incorporated behaviour change techniques and which focused on changing diet behaviour gradually over the 12-week period. The behaviour change planner forms part of a behaviour change intervention which also includes a preparatory workbook exercise and one-to-one action planning sessions and can be customised for each participant. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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19 pages, 9506 KiB  
Article
Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications
by Alessandro Manoni, Alessandro Gumiero, Alessandro Zampogna, Chiara Ciarlo, Lorenzo Panetta, Antonio Suppa, Luigi Della Torre and Fernanda Irrera
Sensors 2022, 22(7), 2566; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072566 - 27 Mar 2022
Cited by 3 | Viewed by 2854
Abstract
In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living [...] Read more.
In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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12 pages, 1038 KiB  
Article
The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults
by Mandy Miller Koop, Anson B. Rosenfeldt, Kelsey Owen, Amanda L. Penko, Matthew C. Streicher, Alec Albright and Jay L. Alberts
Sensors 2022, 22(5), 2009; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052009 - 04 Mar 2022
Cited by 11 | Viewed by 3168
Abstract
Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify [...] Read more.
Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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13 pages, 254 KiB  
Article
Self-Reports in the Field Using Smartwatches: An Open-Source Firmware Solution
by Selina Volsa, Bernad Batinic and Stefan Stieger
Sensors 2022, 22(5), 1980; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051980 - 03 Mar 2022
Cited by 5 | Viewed by 1893
Abstract
In situ self-reports are a useful tool in the social sciences to supplement laboratory experiments. Smartwatches are a promising form factor to realize these methods. However, to date, no user-friendly, general-purpose solution has been available. This article therefore presents a newly developed, free [...] Read more.
In situ self-reports are a useful tool in the social sciences to supplement laboratory experiments. Smartwatches are a promising form factor to realize these methods. However, to date, no user-friendly, general-purpose solution has been available. This article therefore presents a newly developed, free and open-source firmware that facilitates the Experience Sampling Method and other self-report methods on a commercially-available, programmable smartwatch based on the ESP32 microcontroller. In a small-scale pilot study comparing this smartwatch and firmware to an equivalent design on smartphones, participants using the smartwatch showed increased compliance. The presented project demonstrates a useful tool for complementary tools like smartphones for self-reports. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
13 pages, 2752 KiB  
Article
Translational Applications of Wearable Sensors in Education: Implementation and Efficacy
by Brendon Ferrier, Jim Lee, Alex Mbuli and Daniel A. James
Sensors 2022, 22(4), 1675; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041675 - 21 Feb 2022
Cited by 3 | Viewed by 2062
Abstract
Background: Adding new approaches to teaching curriculums can be both expensive and complex to learn. The aim of this research was to gain insight into students’ literacy and confidence in learning sports science with new wearable technologies, specifically a novel program known as [...] Read more.
Background: Adding new approaches to teaching curriculums can be both expensive and complex to learn. The aim of this research was to gain insight into students’ literacy and confidence in learning sports science with new wearable technologies, specifically a novel program known as STEMfit. Methods: A three-phase design was carried out, with 36 students participating and exposed to wearable devices and associated software. This was to determine whether the technology hardware (phase one) and associated software (phase two) were used in a positive way that demonstrated user confidence. Results: Hardware included choosing a scalable wearable device that worked in conjunction with familiar and readily available software (Microsoft Excel) that extracted data through VBA coding. This allowed for students to experience and provide survey feedback on the usability and confidence gained when interacting with the STEMfit program. Outcomes indicated strong acceptance of the program, with high levels of motivation, resulting in a positive uptake of wearable technology as a teaching tool by students. The initial finding of this study offers an opportunity to further test the STEMfit program on other student cohorts as well as testing the scalability of the system into other year groups at the university level. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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12 pages, 5665 KiB  
Article
The Immediate Carryover Effects of Peroneal Functional Electrical Stimulation Differ between People with and without Chronic Ankle Instability
by Uri Gottlieb, Jay R. Hoffman and Shmuel Springer
Sensors 2022, 22(4), 1622; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041622 - 18 Feb 2022
Cited by 2 | Viewed by 3056
Abstract
Chronic ankle instability (CAI) is a common condition that may develop after an ankle sprain. Compared with healthy individuals, those with CAI demonstrate excessive ankle inversion and increased peroneal electromyography (EMG) activity throughout the stance phase of gait, which may put them at [...] Read more.
Chronic ankle instability (CAI) is a common condition that may develop after an ankle sprain. Compared with healthy individuals, those with CAI demonstrate excessive ankle inversion and increased peroneal electromyography (EMG) activity throughout the stance phase of gait, which may put them at greater risk for re-injury. Functional electrical stimulation (FES) of targeted muscles may provide benefits as a treatment modality to stimulate immediate adaptation of the neuromuscular system. The present study investigated the effect of a single, 10 min peroneal FES session on ankle kinematics and peroneal EMG activity in individuals with (n = 24) or without (n = 24) CAI. There were no significant differences in ankle kinematics between the groups before the intervention. However, after the intervention, healthy controls demonstrated significantly less ankle inversion between 0–9% (p = 0.009) and 82–87% (p = 0.011) of the stance phase. Furthermore, a significant within-group difference was observed only in the control group, demonstrating increased ankle eversion between 0–7% (p = 0.011) and 67–81% (p = 0.006) of the stance phase after the intervention. Peroneal EMG activity did not differ between groups or measurements. These findings, which demonstrate that peroneal FES can induce ankle kinematics adaptations during gait, can help to develop future interventions for people with CAI. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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10 pages, 1698 KiB  
Article
Integrated Timing of Stroking, Breathing, and Kicking in Front-Crawl Swimming: A Novel Stroke-by-Stroke Approach Using Wearable Inertial Sensors
by Silvia Fantozzi, Vittorio Coloretti, Maria Francesca Piacentini, Claudio Quagliarotti, Sandro Bartolomei, Giorgio Gatta and Matteo Cortesi
Sensors 2022, 22(4), 1419; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041419 - 12 Feb 2022
Cited by 5 | Viewed by 3086
Abstract
Quantitative evaluation of synergic action among the different body segments is fundamental to swimming performance. The aim of the present study was to develop an easy-to-use tool for stroke-by-stroke evaluation of a swimmer’s integrated timing of stroking, kicking, and breathing. Twelve swimmers were [...] Read more.
Quantitative evaluation of synergic action among the different body segments is fundamental to swimming performance. The aim of the present study was to develop an easy-to-use tool for stroke-by-stroke evaluation of a swimmer’s integrated timing of stroking, kicking, and breathing. Twelve swimmers were evaluated during one trial of 100 m front-crawl swimming at self-selected speed. Five three-axial inertial sensors were mounted on the head, wrists, and ankles. Algorithms for the wrist entry into the water, the lower limb beat during the downward action, and the exit/entry of the face from/into the water were developed. Temporal events identified by video-based technique, using one sagittal moving camera, were assumed as the gold standard. The performance was evaluated in terms of the root-mean-square error, 90th percentile of absolute error, coefficient of variation, Bland–Altman plots, and correlation analysis. Results of all temporal events showed high agreement with the gold standard, confirmed by a root-mean-square error of less than 0.05 s for absolute temporal parameters and less than 0.7% for the percentages of the stroke cycle duration, and with correlation coefficients higher than 0.856. The protocol proposed was not only accurate and reliable, but also user-friendly and as unobtrusive as possible for the swimmer, allowing a stroke-by-stroke analysis during the training session. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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8 pages, 4076 KiB  
Article
Assessment of Thigh Angular Velocity by an Activity Monitor to Describe Sit-to-Stand Performance
by Jochen Klenk, Alassane Ba, Kim S. Sczuka, Urban Daub and Ulrich Lindemann
Sensors 2022, 22(4), 1405; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041405 - 11 Feb 2022
Cited by 1 | Viewed by 1564
Abstract
The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under [...] Read more.
The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under unsupervised daily life conditions. Since thigh orientation from horizontal to vertical is characteristic for STS movement, peak angular velocity (PAV) of the thigh was chosen as the outcome variable. A total of 20 younger and older healthy persons and geriatric patients (mean age: 55.5 ± 20.8 years; 55% women) with a wide range of STS performance were instructed to stand up from a chair at their usual pace. STS performance was measured by an activity monitor, force plates, and an opto-electronic system. The association between PAV measured by the thigh-worn activity monitor and PAV measured by the opto-electronic system (gold standard) was r = 0.74. The association between PAV measured by the thigh-worn activity monitor and peak power measured by force plate and opto-electronic system was r = 0.76. The Intra-Class Coefficient (ICC) of agreement between the 2 trials was ICC(A,1) = 0.76. In this sample of persons with a wide range of physical performance, PAV as measured by a thigh-worn acceleration sensor was a valid and reliable measure of STS performance. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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19 pages, 2158 KiB  
Article
User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion
by Jose Guillermo Colli Alfaro and Ana Luisa Trejos
Sensors 2022, 22(4), 1321; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041321 - 09 Feb 2022
Cited by 24 | Viewed by 3284
Abstract
Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural [...] Read more.
Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. To address this issue, electromyography (EMG)-based gesture recognition systems have been studied as a potential solution for human–machine interface applications. Recent studies have focused on developing user-independent gesture recognition interfaces to reduce calibration times for new users. Unfortunately, given the stochastic nature of EMG signals, the performance of these interfaces is negatively impacted. To address this issue, this work presents a user-independent gesture classification method based on a sensor fusion technique that combines EMG data and inertial measurement unit (IMU) data. The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform seven types of gestures in four different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using three different classification methods. Overall, average classification accuracies in the range of 67.5–84.6% were obtained, with the Adaptive Least-Squares Support Vector Machine model obtaining accuracies as high as 92.9%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural interface that allows better control of wearable mechatronic devices during robot assisted therapies. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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15 pages, 1666 KiB  
Article
Monitoring Particulate Matter with Wearable Sensors and the Influence on Student Environmental Attitudes
by Frances Kane, Joseph Abbate, Eric C. Landahl and Mark J. Potosnak
Sensors 2022, 22(3), 1295; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031295 - 08 Feb 2022
Cited by 6 | Viewed by 3758
Abstract
The mobile monitoring of air pollution is a growing field, prospectively filling in spatial gaps while personalizing air-quality-based risk assessment. We developed wearable sensors to record particulate matter (PM), and through a community science approach, students of partnering Chicago high schools monitored PM [...] Read more.
The mobile monitoring of air pollution is a growing field, prospectively filling in spatial gaps while personalizing air-quality-based risk assessment. We developed wearable sensors to record particulate matter (PM), and through a community science approach, students of partnering Chicago high schools monitored PM concentrations during their commutes over a five- and thirteen-day period. Our main objective was to investigate how mobile monitoring influenced students’ environmental attitudes and we did this by having the students explore the relationship between PM concentrations and urban vegetation. Urban vegetation was approximated with a normalized difference vegetation index (NDVI) using Landsat 8 satellite imagery. While the linear regression for one partner school indicated a negative correlation between PM and vegetation, the other indicated a positive correlation, contrary to our expectations. Survey responses were scored on the basis of their environmental affinity and knowledge. There were no significant differences between cumulative pre- and post-experiment survey responses at Josephinum Academy, and only one weakly significant difference in survey results at DePaul Prep in the Knowledge category. However, changes within certain attitudinal subscales may possibly suggest that students were inclined to practice more sustainable behaviors, but perhaps lacked the resources to do so. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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45 pages, 2864 KiB  
Article
Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions
by Aditi Site, Elena Simona Lohan, Outi Jolanki, Outi Valkama, Rosana Rubio Hernandez, Rita Latikka, Daria Alekseeva, Saigopal Vasudevan, Samuel Afolaranmi, Aleksandr Ometov, Atte Oksanen, Jose Martinez Lastra, Jari Nurmi and Fernando Nieto Fernandez
Sensors 2022, 22(3), 1108; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031108 - 01 Feb 2022
Cited by 7 | Viewed by 9749
Abstract
As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation [...] Read more.
As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated restrictions have exacerbated the loneliness and social-isolation problems. This paper is first and foremost a comprehensive survey of loneliness monitoring and management solutions, from the multidisciplinary perspective of technology, gerontology, socio-psychology, and urban built environment. In addition, our paper also investigates machine learning-based technological solutions with wearable-sensor data, suitable to measure, monitor, manage, and/or diminish the levels of loneliness and social isolation, when one also considers the constraints and characteristics coming from social science, gerontology, and architecture/urban built environments points of view. Compared to the existing state of the art, our work is unique from the cross-disciplinary point of view, because our authors’ team combines the expertise from four distinct domains, i.e., gerontology, social psychology, architecture, and wireless technology in addressing the two inter-related problems of loneliness and social isolation in older adults. This work combines a cross-disciplinary survey of the literature in the four aforementioned domains with a proposed wearable-based technological solution, introduced first as a generic framework and, then, exemplified through a simple proof of concept with dummy data. As the main findings, we provide a comprehensive view on challenges and solutions in utilizing various technologies, particularly those carried by users, also known as wearables, to measure, manage, and/or diminish the social isolation and the perceived loneliness among older adults. In addition, we also summarize the identified solutions which can be used for measuring and monitoring various loneliness- and social isolation-related metrics, and we present and validate, through a simple proof-of-concept mechanism, an approach based on machine learning for predicting and estimating loneliness levels. Open research issues in this field are also discussed. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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15 pages, 2318 KiB  
Article
Textile Slotted Waveguide Antennas for Body-Centric Applications
by Davorin Mikulić, Evita Šopp, Davor Bonefačić and Zvonimir Šipuš
Sensors 2022, 22(3), 1046; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031046 - 28 Jan 2022
Cited by 9 | Viewed by 2994
Abstract
One of the major challenges in the development of wearable antennas is to design an antenna that can at the same time satisfy technical requirements, be aesthetically acceptable, and be suitable for wearable applications. In this paper, a novel wearable antenna is proposed—textile [...] Read more.
One of the major challenges in the development of wearable antennas is to design an antenna that can at the same time satisfy technical requirements, be aesthetically acceptable, and be suitable for wearable applications. In this paper, a novel wearable antenna is proposed—textile realization of a slotted waveguide antenna. The antenna is realized using conductive fabric to manufacture the walls of a rectangular waveguide in which the slots were cut out. All connections and cuts are sewn with conductive thread taking over advantages of the traditional process of manufacturing textile objects. The developed slotted waveguide array prototype, containing three slots and designed for operation in the 5.8-GHz ISM band, is experimentally characterized and compared to an equivalent metallic antenna. The achieved operating bandwidth is larger than 300 MHz in both cases. The measured gain of a textile slotted waveguide array is around 9 dBi with a radiation efficiency larger than 50% in the whole operating bandwidth, i.e., the textile array showed a 2 dB lower gain in comparison to the metallic counterpart. The gain is stable in the whole bandwidth and the radiation patterns do not differ. The results demonstrated that such textile antennas are suitable for body-centric communication and sensor systems and can be integrated into clothing, e.g., into a smart safety vest or into a uniform. Further analysis of various realizations of slotted waveguide antennas is presented showing that different versions of the proposed antenna can be used in all three off-body, on-body, and in-body communication scenarios. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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30 pages, 2298 KiB  
Article
Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease
by Barry R. Greene, Isabella Premoli, Killian McManus, Denise McGrath and Brian Caulfield
Sensors 2022, 22(1), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010054 - 22 Dec 2021
Cited by 8 | Viewed by 3836
Abstract
People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, [...] Read more.
People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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18 pages, 5417 KiB  
Article
Scoring Performance on the Y-Balance Test Using a Deep Learning Approach
by Manuel Gil-Martín, William Johnston, Rubén San-Segundo and Brian Caulfield
Sensors 2021, 21(21), 7110; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217110 - 26 Oct 2021
Cited by 4 | Viewed by 4175
Abstract
The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during [...] Read more.
The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during the movement. This paper evaluates several signal processing techniques to extract relevant information to feed the deep neural network. This evaluation was performed using a state-of-the-art human activity recognition system based on recurrent neural networks (RNNs). This deep neural network includes long short-term memory (LSTM) layers to learn features from time series by modeling temporal patterns and an additional fully connected layer to estimate the NRD (normalized by the leg length). All analyses were carried out using a dataset with YBT assessments from 407 subjects, including young and middle-aged volunteers and athletes from different sports. This dataset allowed developing a global and robust solution for scoring the YBT in a wide range of applications. The experimentation setup considered a 10-fold subject-wise cross-validation using training, validation, and testing subsets. The mean absolute percentage error (MAPE) obtained was 7.88 ± 0.20%. Moreover, this work proposes specific regression systems to estimate the NRD for each direction separately, obtaining an average MAPE of 7.33 ± 0.26%. This deep learning approach was compared to a previous work using dynamic time warping and k-NN algorithms, obtaining a relative MAPE reduction of 10%. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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22 pages, 12808 KiB  
Article
Discriminative Mobility Characteristics between Neurotypical Young, Middle-Aged, and Older Adults Using Wireless Inertial Sensors
by Clayton W. Swanson and Brett W. Fling
Sensors 2021, 21(19), 6644; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196644 - 06 Oct 2021
Cited by 3 | Viewed by 1670
Abstract
Age-related mobility research often highlights significant mobility differences comparing neurotypical young and older adults, while neglecting to report mobility outcomes for middle-aged adults. Moreover, these analyses regularly do not determine which measures of mobility can discriminate groups into their age brackets. Thus, the [...] Read more.
Age-related mobility research often highlights significant mobility differences comparing neurotypical young and older adults, while neglecting to report mobility outcomes for middle-aged adults. Moreover, these analyses regularly do not determine which measures of mobility can discriminate groups into their age brackets. Thus, the current study aimed to provide a comprehensive analysis for commonly performed aspects of mobility (walking, turning, sit-to-stand, and balance) to determine which variables were significantly different and furthermore, able to discriminate between neurotypical young adults (YAs), middle-aged adults (MAAs), and older adults (OAs). This study recruited 20 YAs, 20 MAAs, and 20 OAs. Participants came into the laboratory and completed mobility testing while wearing wireless inertial sensors. Mobility tests assessed included three distinct two-minute walks, 360° turns, five times sit-to-stands, and a clinical balance test, capturing 99 distinct mobility metrics. Of the various mobility tests assessed, only 360° turning measures demonstrated significance between YAs and MAAs, although the capacity to discriminate between groups was achieved for gait and turning measures. A variety of mobility measures demonstrated significance between MAAs and OAs, and furthermore discrimination was achieved for each mobility test. These results indicate greater mobility differences between MAAs and OAs, although discrimination is achievable for both group comparisons. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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12 pages, 2781 KiB  
Article
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
by Eoin Brophy, Maarten De Vos, Geraldine Boylan and Tomás Ward
Sensors 2021, 21(18), 6311; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186311 - 21 Sep 2021
Cited by 25 | Viewed by 4190
Abstract
Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors [...] Read more.
Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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12 pages, 1080 KiB  
Article
Limitations of Foot-Worn Sensors for Assessing Running Power
by Tobias Baumgartner, Steffen Held, Stefanie Klatt and Lars Donath
Sensors 2021, 21(15), 4952; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154952 - 21 Jul 2021
Cited by 9 | Viewed by 3494
Abstract
Running power as measured by foot-worn sensors is considered to be associated with the metabolic cost of running. In this study, we show that running economy needs to be taken into account when deriving metabolic cost from accelerometer data. We administered an experiment [...] Read more.
Running power as measured by foot-worn sensors is considered to be associated with the metabolic cost of running. In this study, we show that running economy needs to be taken into account when deriving metabolic cost from accelerometer data. We administered an experiment in which 32 experienced participants (age = 28 ± 7 years, weekly running distance = 51 ± 24 km) ran at a constant speed with modified spatiotemporal gait characteristics (stride length, ground contact time, use of arms). We recorded both their metabolic costs of transportation, as well as running power, as measured by a Stryd sensor. Purposely varying the running style impacts the running economy and leads to significant differences in the metabolic cost of running (p < 0.01). At the same time, the expected rise in running power does not follow this change, and there is a significant difference in the relation between metabolic cost and power (p < 0.001). These results stand in contrast to the previously reported link between metabolic and mechanical running characteristics estimated by foot-worn sensors. This casts doubt on the feasibility of measuring running power in the field, as well as using it as a training signal. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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17 pages, 1194 KiB  
Article
Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson’s Disease
by Stefano Filippo Castiglia, Antonella Tatarelli, Dante Trabassi, Roberto De Icco, Valentina Grillo, Alberto Ranavolo, Tiwana Varrecchia, Fabrizio Magnifica, Davide Di Lenola, Gianluca Coppola, Donatella Ferrari, Alessandro Denaro, Cristina Tassorelli and Mariano Serrao
Sensors 2021, 21(10), 3449; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103449 - 15 May 2021
Cited by 15 | Viewed by 2922
Abstract
The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. [...] Read more.
The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQAdet in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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Review

Jump to: Research

39 pages, 5132 KiB  
Review
Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review
by Azadeh Kian, Giwantha Widanapathirana, Anna M. Joseph, Daniel T. H. Lai and Rezaul Begg
Sensors 2022, 22(6), 2244; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062244 - 14 Mar 2022
Cited by 5 | Viewed by 5019
Abstract
Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of [...] Read more.
Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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29 pages, 1177 KiB  
Review
The Use of Wearable Sensor Technology to Detect Shock Impacts in Sports and Occupational Settings: A Scoping Review
by Ingrid Eitzen, Julie Renberg and Hilde Færevik
Sensors 2021, 21(15), 4962; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154962 - 21 Jul 2021
Cited by 15 | Viewed by 5280
Abstract
Shock impacts during activity may cause damage to the joints, muscles, bones, or inner organs. To define thresholds for tolerable impacts, there is a need for methods that can accurately monitor shock impacts in real-life settings. Therefore, the main aim of this scoping [...] Read more.
Shock impacts during activity may cause damage to the joints, muscles, bones, or inner organs. To define thresholds for tolerable impacts, there is a need for methods that can accurately monitor shock impacts in real-life settings. Therefore, the main aim of this scoping review was to present an overview of existing methods for assessments of shock impacts using wearable sensor technology within two domains: sports and occupational settings. Online databases were used to identify papers published in 2010–2020, from which we selected 34 papers that used wearable sensor technology to measure shock impacts. No studies were found on occupational settings. For the sports domain, accelerometry was the dominant type of wearable sensor technology utilized, interpreting peak acceleration as a proxy for impact. Of the included studies, 28 assessed foot strike in running, head impacts in invasion and team sports, or different forms of jump landings or plyometric movements. The included studies revealed a lack of consensus regarding sensor placement and interpretation of the results. Furthermore, the identified high proportion of validation studies support previous concerns that wearable sensors at present are inadequate as a stand-alone method for valid and accurate data on shock impacts in the field. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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17 pages, 469 KiB  
Review
Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review
by Blake Anthony Hickey, Taryn Chalmers, Phillip Newton, Chin-Teng Lin, David Sibbritt, Craig S. McLachlan, Roderick Clifton-Bligh, John Morley and Sara Lal
Sensors 2021, 21(10), 3461; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103461 - 16 May 2021
Cited by 76 | Viewed by 17754
Abstract
Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart [...] Read more.
Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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