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Wearable Sensing Technology for Physiological and Behavioral Human Monitoring

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 33149

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Dr. Nicola Carbonaro

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Information Engineering Department and Research Center "E. Piaggio”, University of Pisa, 56123 Pisa, Italy
Interests: hardware and software development for wearable sensing technology for physiological and behavioral human monitoring
Special Issues, Collections and Topics in MDPI journals

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Dear Colleague,

Recent technological developments have enabled wearable sensors that are increasingly robust and reliable for the acquisition of fundamental parameters for the analysis of people's behavior and health. Wearable systems can be used during common daily activities, thus offering the opportunity to record user data in real time continuously and without discomfort and, consequently, to offer new treatment and assistance opportunities to those at risk. Thanks to the small size and the ability to integrate into normal body-worn objects, such as watches, glasses, bracelets, and clothing, wearable sensors allow for the continuous long-term monitoring of human physiological and behavioral parameters during common activities (work, sport, and spare time) and in all those contexts outside the equipped laboratories. Wearable technologies are also able to assist users by giving them information on their health conditions (e.g., acquiring heart rate, respiration, biopotential, and biomarkers) and/or their behavior (e.g., registering body movements, gait analysis, and activity tracking) through direct feedback or through the supervision of specialized operators connected remotely with the subjects.

This Special Issue is intended to explore recent advances in wearable sensing technology for human physiological and behavioral monitoring and to report on challenges related to the application of these devices for the prevention and treatment of the subject's health conditions.

Dr. Nicola Carbonaro
Guest Editor

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Keywords

wearable sensors
new sensing concepts
smart textiles
processing methods for wearable sensors
gait analysis
activity recognition
ECG
biopotential
respiration
rehabilitation
self-assessment

Related Special Issue

Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition in Sensors (4 articles)

Published Papers (11 papers)

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Research

Jump to: Review

16 pages, 2118 KiB

Open AccessArticle

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate

by Kamran Ali, Sachin Shah and Charles E. Hughes

Sensors 2023, 23(14), 6572; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146572 - 21 Jul 2023

Cited by 1 | Viewed by 1074

Abstract

Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settings is needed. This paper presents a framework for recognizing the affective state of children with ASD in an in-the-wild setting using heart rate (HR) information. More specifically, an algorithm is developed that can classify a participant’s emotion as positive, negative, or neutral by analyzing the heart rate signal acquired from a smartwatch. The heart rate data are obtained in real time using a smartwatch application while the child learns to code a robot and interacts with an avatar. The avatar assists the child in developing communication skills and programming the robot. In this paper, we also present a semi-automated annotation technique based on facial expression recognition for the heart rate data. The HR signal is analyzed to extract features that capture the emotional state of the child. Additionally, in this paper, the performance of a raw HR-signal-based emotion classification algorithm is compared with a classification approach based on features extracted from HR signals using discrete wavelet transform (DWT). The experimental results demonstrate that the proposed method achieves comparable performance to state-of-the-art HR-based emotion recognition techniques, despite being conducted in an uncontrolled setting rather than a controlled lab environment. The framework presented in this paper contributes to the real-world affect analysis of children with ASD using HR information. By enabling emotion recognition in uncontrolled settings, this approach has the potential to improve the monitoring and understanding of the emotional well-being of children with ASD in their daily lives. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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13 pages, 778 KiB

Open AccessArticle

PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values

by Masahiro Okamoto and Kazuya Murao

Sensors 2023, 23(4), 1782; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041782 - 05 Feb 2023

Cited by 1 | Viewed by 2386

Abstract

The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user’s wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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12 pages, 2901 KiB

Open AccessArticle

The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study

by Victoriya Smirnova, Regina Khamatnurova, Nikita Kharin, Elena Yaikova, Tatiana Baltina and Oskar Sachenkov

Sensors 2022, 22(19), 7178; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197178 - 21 Sep 2022

Cited by 2 | Viewed by 2316

Abstract

The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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14 pages, 694 KiB

Open AccessArticle

The Effects of a Visual Stimuli Training Program on Reaction Time, Cognitive Function, and Fitness in Young Soccer Players

by Georgia Theofilou, Ioannis Ladakis, Charikleia Mavroidi, Vasileios Kilintzis, Theodoros Mirachtsis, Ioanna Chouvarda and Evangelia Kouidi

Sensors 2022, 22(17), 6680; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176680 - 03 Sep 2022

Cited by 8 | Viewed by 5076

Abstract

The purpose of the present study was to examine whether a visual stimuli program during soccer training can affect reaction time (RT), cognitive function, and physical fitness in adolescent soccer players. Thirty-eight male soccer players aged 10–15 were randomly assigned to either the intervention (Group A) or the control group (Group B). At baseline and at the end of the 6-month study FITLIGHT Trainer, the Cognitive Function Scanner Mobile Test Suite, a Virtual Reality (VR) game, and the ALPHA—Fitness and the Eurofit test batteries were used to measure participants’ abilities. After the baseline assessment, Group A followed their regular soccer training combined with a visual stimuli program, while Group B continued their regular soccer training program alone for 6 months. At the end of the 6-month study, Group A showed statistically significant improvements in simple RT by 11.8% (p = 0.002), repeated sprints by 13.4% (p ≤ 0.001), and Pen-to-Point Cognitive Function by 71.62% (p < 0.001) and 72.51% for dominant and non-dominant hands, respectively. However, a between-groups analysis showed that there was no statistically significant difference between the two groups in most of the measurements studied. In conclusion, a visual stimuli training program does not seem to add any value to the traditional soccer training program for adolescents. Nevertheless, this study helps to underline the potential of newly emerging technology as a tool for the assessment of RT. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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22 pages, 13544 KiB

Open AccessArticle

Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers

by Jorge Herranz Olazábal, Fokko Wieringa, Evelien Hermeling and Chris Van Hoof

Sensors 2022, 22(16), 6059; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166059 - 13 Aug 2022

Cited by 4 | Viewed by 2047

Abstract

Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R² values of rPPG with rSPG (R² = 0.99) and ITA (R² = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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14 pages, 2634 KiB

Open AccessArticle

Machine Learning-Based Predicted Age of the Elderly on the Instrumented Timed Up and Go Test and Six-Minute Walk Test

by Jeong Bae Ko, Jae Soo Hong, Young Sub Shin and Kwang Bok Kim

Sensors 2022, 22(16), 5957; https://0-doi-org.brum.beds.ac.uk/10.3390/s22165957 - 09 Aug 2022

Cited by 4 | Viewed by 2049

Abstract

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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16 pages, 7277 KiB

Open AccessArticle

Wearable Cardiorespiratory Sensors for Aerospace Applications

by Nichakorn Pongsakornsathien, Alessandro Gardi, Yixiang Lim, Roberto Sabatini and Trevor Kistan

Sensors 2022, 22(13), 4673; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134673 - 21 Jun 2022

Cited by 2 | Viewed by 1565

Abstract

Emerging Air Traffic Management (ATM) and avionics human–machine system concepts require the real-time monitoring of the human operator to support novel task assessment and system adaptation features. To realise these advanced concepts, it is essential to resort to a suite of sensors recording neurophysiological data reliably and accurately. This article presents the experimental verification and performance characterisation of a cardiorespiratory sensor for ATM and avionics applications. In particular, the processed physiological measurements from the designated commercial device are verified against clinical-grade equipment. Compared to other studies which only addressed physical workload, this characterisation was performed also looking at cognitive workload, which poses certain additional challenges to cardiorespiratory monitors. The article also addresses the quantification of uncertainty in the cognitive state estimation process as a function of the uncertainty in the input cardiorespiratory measurements. The results of the sensor verification and of the uncertainty propagation corroborate the basic suitability of the commercial cardiorespiratory sensor for the intended aerospace application but highlight the relatively poor performance in respiratory measurements during a purely mental activity. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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10 pages, 728 KiB

Open AccessArticle

Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

by Pascale Juneau, Natalie Baddour, Helena Burger, Andrej Bavec and Edward D. Lemaire

Sensors 2022, 22(5), 1749; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051749 - 23 Feb 2022

Cited by 6 | Viewed by 1999

Abstract

The 6-min walk test (6MWT) is commonly used to assess a person’s physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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19 pages, 4772 KiB

Open AccessArticle

Fusion of Wearable Kinetic and Kinematic Sensors to Estimate Triceps Surae Work during Outdoor Locomotion on Slopes

by Sara E. Harper, Dylan G. Schmitz, Peter G. Adamczyk and Darryl G. Thelen

Sensors 2022, 22(4), 1589; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041589 - 18 Feb 2022

Cited by 10 | Viewed by 6134

Abstract

Muscle–tendon power output is commonly assessed in the laboratory through the work loop, a paired analysis of muscle force and length during a cyclic task. Work-loop analysis of muscle–tendon function in out-of-lab conditions has been elusive due to methodological limitations. In this work, we combined kinetic and kinematic measures from shear wave tensiometry and inertial measurement units, respectively, to establish a wearable system for estimating work and power output from the soleus and gastrocnemius muscles during outdoor locomotion. Across 11 healthy young adults, we amassed 4777 strides of walking on slopes from −10° to +10°. Results showed that soleus work scales with incline, while gastrocnemius work is relatively insensitive to incline. These findings agree with previous results from laboratory-based studies while expanding technological capabilities by enabling wearable analysis of muscle–tendon kinetics. Applying this system in additional settings and activities could improve biomechanical knowledge and evaluation of protocols in scenarios such as rehabilitation, device design, athletics, and military training. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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12 pages, 4272 KiB

Open AccessArticle

Non-Intrusive Contact Respiratory Sensor for Vehicles

by Quentin Meteier, Michiel Kindt, Leonardo Angelini, Omar Abou Khaled and Elena Mugellini

Sensors 2022, 22(3), 880; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030880 - 24 Jan 2022

Cited by 2 | Viewed by 2431

Abstract

In this work, we propose a low-cost solution capable of collecting the driver’s respiratory signal in a robust and non-intrusive way by contact with the chest and abdomen. It consists of a microcontroller and two piezoelectric sensors with their respective 3D printed plastic housings attached to the seat belt. An iterative process was conducted to find the optimal shape of the sensor housing. The location of the sensors can be easily adapted by sliding them along the seat belt. A few participants took part in three test sessions in a driving simulator. They had to perform various activities: resting, deep breathing, manual driving, and a non-driving-related task during automated driving. The subjects’ breathing rates were calculated from raw data collected with a reference chest belt, each sensor alone, and the fusion of the two. Results indicate that respiratory rate could be assessed from a single sensor located on the chest with an average absolute error of 0.92 min⁻¹ across all periods, dropping to 0.13 min⁻¹ during deep breathing. Sensor fusion did not improve system performance. A 4-pole filter with a cutoff frequency of 1 Hz emerged as the best option to minimize the error during the different periods. The results suggest that such a system could be used to assess the driver’s breathing rate while performing various activities in a vehicle. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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Review

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26 pages, 1303 KiB

Open AccessReview

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

by Lauren E. Rentz, Hana K. Ulman and Scott M. Galster

Sensors 2021, 21(15), 5071; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155071 - 27 Jul 2021

Cited by 23 | Viewed by 4612

Abstract

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to “measure” sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success. Full article

(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring)

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