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Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition
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Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition

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 33935

Special Issue Editor

Dr. Nicola Carbonaro

E-Mail Website
Guest Editor
Research Center “E. Piaggio”, University of Pisa, Largo L. Lazzerino, 1, 56122 Pisa, Italy
Interests: wearable sensors; human movement reconstruction; inertial sensors; rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technological developments have enabled the creation of wearable sensors that are increasingly robust and reliable for the acquisition of fundamental parameters that can be used to analyze 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 without discomfort and consequently to offer new treatment and assistance opportunities to those at risk. Thanks to their small size and ability to be integrated 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 that are remotely connected to the subjects.

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

Dr. Nicola Carbonaro
Guest Editor

Manuscript Submission Information

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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

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Related Special Issue

Published Papers (7 papers)

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Research

17 pages, 4461 KiB  
Article
A Novel Wearable Sensor for Measuring Respiration Continuously and in Real Time
by Amjad Ali, Yang Wei, Yomna Elsaboni, Jack Tyson, Harry Akerman, Alexander I. R. Jackson, Rod Lane, Daniel Spencer and Neil M. White
Sensors 2024, 24(20), 6513; https://doi.org/10.3390/s24206513 - 10 Oct 2024
Viewed by 419
Abstract
In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor [...] Read more.
In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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18 pages, 3343 KiB  
Article
Experimental Study of Long Short-Term Memory and Transformer Models for Fall Detection on Smartwatches
by Syed Tousiful Haque, Minakshi Debnath, Awatif Yasmin, Tarek Mahmud and Anne Hee Hiong Ngu
Sensors 2024, 24(19), 6235; https://doi.org/10.3390/s24196235 - 26 Sep 2024
Viewed by 586
Abstract
Falls are the second leading cause of unintentional injury deaths worldwide. While numerous wearable fall detection devices incorporating AI models have been developed, none of them are used successfully in a fall detection application running on commodity-based smartwatches in real time. The system [...] Read more.
Falls are the second leading cause of unintentional injury deaths worldwide. While numerous wearable fall detection devices incorporating AI models have been developed, none of them are used successfully in a fall detection application running on commodity-based smartwatches in real time. The system misses some falls, and generates an annoying amount of False Positives for practical use. We have investigated and experimented with an LSTM model for fall detection on a smartwatch. Even though the LSTM model has high accuracy during offline testing, the good performance of offline LSTM models cannot be translated to the equivalence of real-time performance. Transformers, on the other hand, can learn long-sequence data and patterns intrinsic to the data due to their self-attention mechanism. This paper compares three variants of LSTM and two variants of Transformer models for learning fall patterns. We trained all models using fall and activity data from three datasets, and the real-time testing of the model was performed using the SmartFall App. Our findings showed that in the offline training, the CNN-LSTM model was better than the Transformer model for all the datasets. However, the Transformer is a preferable choice for deployment in real-time fall detection applications. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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19 pages, 2795 KiB  
Article
How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression?
by Pranav Madhav Kuber, Abhineet Rajendra Kulkarni and Ehsan Rashedi
Sensors 2024, 24(18), 5971; https://doi.org/10.3390/s24185971 - 14 Sep 2024
Viewed by 483
Abstract
Forecasting can be utilized to predict future trends in physiological demands, which can be beneficial for developing effective interventions. This study implemented forecasting models to predict fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were utilized from [...] Read more.
Forecasting can be utilized to predict future trends in physiological demands, which can be beneficial for developing effective interventions. This study implemented forecasting models to predict fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were utilized from nine recruited participants who performed 45° trunk flexion tasks intermittently with and without assistance until they reached medium-high exertion in the low-back region. Two forecasting algorithms, Autoregressive Integrated Moving Average (ARIMA) and Facebook Prophet, were implemented using perceived fatigue levels alone, and with external features of low-back muscle activity. Findings showed that univariate models without external features performed better with the Prophet model having the lowest mean (SD) of root mean squared error (RMSE) across participants of 0.62 (0.24) and 0.67 (0.29) with and without EXO-assisted tasks, respectively. Temporal effects of BSIE on delaying fatigue progression were then evaluated by forecasting back fatigue up to 20 trials. The slope of fatigue progression for 20 trials without assistance was ~48–52% higher vs. with assistance. Median benefits of 54% and 43% were observed for ARIMA (with external features) and Prophet algorithms, respectively. This study demonstrates some potential applications for forecasting models for workforce health monitoring, intervention assessment, and injury prevention. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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14 pages, 992 KiB  
Article
Thermography Sensor to Assess Motor and Sensitive Neuromuscular Sequels of Brain Damage
by Alessio Cabizosu, Daniele Grotto, Alberto López López and Raúl Castañeda Vozmediano
Sensors 2024, 24(6), 1723; https://doi.org/10.3390/s24061723 - 7 Mar 2024
Viewed by 1042
Abstract
Introduction. The aim of this study was to observe the validity, diagnostic capacity, and reliability of the thermographic technique in the analysis of sensitive and motor sequelae in patients with chronic brain damage. Method. A longitudinal descriptive observational study was performed. Forty-five people [...] Read more.
Introduction. The aim of this study was to observe the validity, diagnostic capacity, and reliability of the thermographic technique in the analysis of sensitive and motor sequelae in patients with chronic brain damage. Method. A longitudinal descriptive observational study was performed. Forty-five people with impairment in at least one anatomical region participated in and completed this study. All patients who had become infected by SARS-CoV-2 in the past year were excluded. Thermographic measurement was conducted, and the Modified Ashworth Scale and Pressure Pain Threshold was analyzed. Results. A high correlation between two times of thermography data was observed. The Spearman correlations obtained between the Ashworth score on each leg and the temperature given by thermography were all significant. Discussion and conclusions. Despite the above, the Spearman correlations obtained between the PPT in each leg and the temperature offered by thermography were not significant in any of the measurements. For this reason, thermography is a potential tool for the diagnosis and assessment of neuromuscular motor sequelae, but not for sensitive sequelae, after brain injury. Nevertheless, for the time being, no statistical relationship has been observed between the data reported by thermography and PPT; thus, future studies are needed to further investigate these results. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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16 pages, 2523 KiB  
Article
Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography
by Kyle A. Kainec, Jamie Caccavaro, Morgan Barnes, Chloe Hoff, Annika Berlin and Rebecca M. C. Spencer
Sensors 2024, 24(2), 635; https://doi.org/10.3390/s24020635 - 19 Jan 2024
Cited by 8 | Viewed by 26638
Abstract
The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. [...] Read more.
The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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12 pages, 2448 KiB  
Article
Assessment of Cardiorespiratory and Metabolic Contributions in an Extreme Intensity CrossFit® Benchmark Workout
by Manoel Rios, Klaus Magno Becker, Filipa Cardoso, David B. Pyne, Victor Machado Reis, Daniel Moreira-Gonçalves and Ricardo J. Fernandes
Sensors 2024, 24(2), 513; https://doi.org/10.3390/s24020513 - 14 Jan 2024
Cited by 3 | Viewed by 2100
Abstract
Our purpose was to characterize the oxygen uptake kinetics (VO2), energy systems contributions and total energy expenditure during a CrossFit® benchmark workout performed in the extreme intensity domain. Fourteen highly trained male CrossFitters, aged 28.3 ± 5.4 years, with height [...] Read more.
Our purpose was to characterize the oxygen uptake kinetics (VO2), energy systems contributions and total energy expenditure during a CrossFit® benchmark workout performed in the extreme intensity domain. Fourteen highly trained male CrossFitters, aged 28.3 ± 5.4 years, with height 177.8 ± 9.4 cm, body mass 87.9 ± 10.5 kg and 5.6 ± 1.8 years of training experience, performed the Isabel workout at maximal exertion. Cardiorespiratory variables were measured at baseline, during exercise and the recovery period, with blood lactate and glucose concentrations, including the ratings of perceived exertion, measured pre- and post-workout. The Isabel workout was 117 ± 10 s in duration and the VO2 peak was 47.2 ± 4.7 mL·kg−1·min−1, the primary component amplitude was 42.0 ± 6.0 mL·kg−1·min−1, the time delay was 4.3 ± 2.2 s and the time constant was 14.2 ± 6.0 s. The accumulated VO2 (0.6 ± 0.1 vs. 4.8 ± 1.0 L·min−1) value post-workout increased substantially when compared to baseline. Oxidative phosphorylation (40%), glycolytic (45%) and phosphagen (15%) pathways contributed to the 245 ± 25 kJ total energy expenditure. Despite the short ~2 min duration of the Isabel workout, the oxygen-dependent and oxygen-independent metabolism energy contributions to the total metabolic energy release were similar. The CrossFit® Isabel requires maximal effort and the pattern of physiological demands identifies this as a highly intensive and effective workout for developing fitness and conditioning for sports. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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14 pages, 3644 KiB  
Article
BCG Signal Quality Assessment Based on Time-Series Imaging Methods
by Sungtae Shin, Soonyoung Choi, Chaeyoung Kim, Azin Sadat Mousavi, Jin-Oh Hahn, Sehoon Jeong and Hyundoo Jeong
Sensors 2023, 23(23), 9382; https://doi.org/10.3390/s23239382 - 24 Nov 2023
Viewed by 1573
Abstract
This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the [...] Read more.
This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts. This distortion leads to errors in blood pressure estimation, thereby lowering the performance of blood pressure measurement based on BCG. In this study, to prevent such performance degradation, a binary classification model was created to distinguish between high-quality versus low-quality BCG signals. To estimate the most accurate model, four time-series imaging methods (recurrence plot, the Gramain angular summation field, the Gramain angular difference field, and the Markov transition field) were studied to convert the temporal BCG signal associated with each heartbeat into a 448 × 448 pixel image, and the image was classified using CNN models such as ResNet, SqueezeNet, DenseNet, and LeNet. A total of 9626 BCG beats were used for training, validation, and testing. The experimental results showed that the ResNet and SqueezeNet models with the Gramain angular difference field method achieved a binary classification accuracy of up to 87.5%. Full article
(This article belongs to the Special Issue Wearable Sensing Technology for Physiological and Behavioral Human Monitoring—2nd Edition)
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