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Wearable Sensor Technologies for Physiological and Environmental 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 (25 December 2023) | Viewed by 13146

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Special Issue Editors

Prof. Dr. Daniel Sánchez-Morillo

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Guest Editor

1. Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de Cádiz, 11519 Cádiz, Spain
2. Biomedical Engineering and Telemedicine Research Group, Universidad de Cádiz, 11510 Cádiz, Spain
3. Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), 11009 Cadiz, Spain
Interests: biomedical signal and image processing; machine learning; e-health; ambient assisted living; computational pathology; predictive analytics in healthcare

Dr. Priego-Torres Blanca Maria

E-Mail Website
Guest Editor

1. Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de Cádiz, 11519 Cádiz, Spain
2. Biomedical Engineering and Telemedicine Research Group, Universidad de Cádiz, 11510 Cádiz, Spain
3. Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), 11009 Cadiz, Spain
Interests: biomedical image processing; machine learning; computational pathology; computer vision; hyperspectral imaging

Special Issue Information

Dear Colleagues,

Physiological processes in the human body can generate many types of accessible biomedical signals, including chemical, electrical, and physical. In addition, data from the outer environment can provide valuable information for certain purposes. Wearable devices incorporating noninvasive sensors, data processing modules, and wireless data transmission capabilities allow for real-time monitoring of physiological and environmental variables, enabling real-time feedback, multi-domain decision support, and decentralized access to information.

Nowadays, signal monitoring by using wearable sensors, as well as the processing, analysis, and transmission of these signals, is essential to gather reliable information that enables decision making in a plethora of domains. Indeed, the new generation of wearable technology is becoming the natural path to full IoT deployment.

This Special Issue aims to promote high-quality scholarly papers that bring out emerging wearable devices applications, techniques, and algorithms not only in the health field but in education, leisure, and other domains, especially to address present and future challenges with breakthroughs in decision making, well-being, consumer behaviour, utility, and big data analytics.

Prof. Dr. Daniel Sánchez-Morillo
Dr. Priego-Torres Blanca Maria
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Bell Ding via <[email protected]>

Manuscript Submission Information

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

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

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

Keywords

wearable sensors
sensing technologies
wearable IoT sensors
sensors signal processing and transmission
energy-efficient wearable systems
multimodality fusion techniques
machine learning for signal processing
deep learning and biomedical signal processing
algorithms for personalized medical assessment
emotion recognition from physiological signals
physical activity assessment
wearable devices and algorithms in education
predictive analytics in telemedicine environments
other emerging applications of biomedical signal processing

Published Papers (7 papers)

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Research

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19 pages, 4302 KiB

Open AccessArticle

Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary School

by Victor Juan Fuentes-Martinez, Samuel Romero, Miguel Angel Lopez-Gordo, Jesus Minguillon and Manuel Rodríguez-Álvarez

Sensors 2023, 23(23), 9361; https://0-doi-org.brum.beds.ac.uk/10.3390/s23239361 - 23 Nov 2023

Viewed by 937

Abstract

The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students’ attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students’ attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12–30 Hz) and students’ academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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13 pages, 1532 KiB

Open AccessArticle

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

by Elaine M. Bochniewicz, Geoff Emmer, Alexander W. Dromerick, Jessica Barth and Peter S. Lum

Sensors 2023, 23(6), 3111; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063111 - 14 Mar 2023

Cited by 2 | Viewed by 1798

Abstract

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3–85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4–72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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15 pages, 1077 KiB

Open AccessArticle

Reliability of a Wearable Motion Tracking System for the Clinical Evaluation of a Dynamic Cervical Spine Function

by Hamed Hani, Reid Souchereau, Anas Kachlan, Jonathan Dufour, Alexander Aurand, Prasath Mageswaran, Madison Hyer and William Marras

Sensors 2023, 23(3), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031448 - 28 Jan 2023

Cited by 2 | Viewed by 1351

Abstract

Neck pain is a common cause of disability worldwide. Lack of objective tools to quantify an individual’s functional disability results in the widespread use of subjective assessments to measure the limitations in spine function and the response to interventions. This study assessed the reliability of the quantifying neck function using a wearable cervical motion tracking system. Three novice raters recorded the neck motion assessments on 20 volunteers using the device. Kinematic features from the signals in all three anatomical planes were extracted and used as inputs to repeated measures and mixed-effects regression models to calculate the intraclass correlation coefficients (ICCs). Cervical spine-specific kinematic features indicated good and excellent inter-rater and intra-rater reliability for the most part. For intra-rater reliability, the ICC values varied from 0.85 to 0.95, and for inter-rater reliability, they ranged from 0.7 to 0.89. Overall, velocity measures proved to be more reliable compared to other kinematic features. This technique is a trustworthy tool for evaluating neck function objectively. This study showed the potential for cervical spine-specific kinematic measurements to deliver repeatable and reliable metrics to evaluate clinical performance at any time points. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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18 pages, 1973 KiB

Open AccessArticle

Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

by Stephen Ward, Sijung Hu and Massimiliano Zecca

Sensors 2023, 23(3), 1416; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031416 - 27 Jan 2023

Cited by 1 | Viewed by 1273

Abstract

A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 ± 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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9 pages, 1657 KiB

Open AccessFeature PaperArticle

Effect of a Passive Exosuit on Sit-to-Stand Performance in Geriatric Patients Measured by Body-Worn Sensors—A Pilot Study

by Ulrich Lindemann, Jana Krespach, Urban Daub, Marc Schneider, Kim S. Sczuka and Jochen Klenk

Sensors 2023, 23(2), 1032; https://0-doi-org.brum.beds.ac.uk/10.3390/s23021032 - 16 Jan 2023

Viewed by 1576

Abstract

Standing up from a seated position is a prerequisite for any kind of physical mobility but many older persons have problems with the sit-to-stand (STS) transfer. There are several exosuits available for industrial work, which might be adapted to the needs of older persons to support STS transfers. However, objective measures to quantify and evaluate such systems are needed. The aim of this study was to quantify the possible support of an exosuit during the STS transfer of geriatric patients. Twenty-one geriatric patients with a median age of 82 years (1.–3.Q. 79–84 years) stood up at a normal pace (1) from a chair without using armrests, (2) with using armrests and (3) from a bed with pushing off, each condition with and without wearing an exosuit. Peak angular velocity of the thighs was measured by body-worn sensors. It was higher when standing up with exosuit support from a bed (92.6 (1.–3.Q. 84.3–116.2)°/s versus 79.7 (1.–3.Q. 74.6–98.2)°/s; p = 0.014) and from a chair with armrests (92.9 (1.–3.Q. 78.3–113.0)°/s versus 77.8 (1.–3.Q. 59.3–100.7)°/s; p = 0.089) compared to no support. There was no effect of the exosuit when standing up from a chair without using armrests. In general, it was possible to quantify the support of the exosuit using sensor-measured peak angular velocity. These results suggest that depending on the STS condition, an exosuit can support older persons during the STS transfer. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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12 pages, 2031 KiB

Open AccessArticle

Machine-Learning Classification of Pulse Waveform Quality

by Te Ouyoung, Wan-Ling Weng, Ting-Yu Hu, Chia-Chien Lee, Li-Wei Wu and Hsin Hsiu

Sensors 2022, 22(22), 8607; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228607 - 08 Nov 2022

Viewed by 2351

Abstract

Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin–sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin–surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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Review

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41 pages, 1436 KiB

Open AccessReview

The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review

by Gemma L. Bate, Cameron Kirk, Rana Z. U. Rehman, Yu Guan, Alison J. Yarnall, Silvia Del Din and Rachael A. Lawson

Sensors 2023, 23(10), 4881; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104881 - 18 May 2023

Cited by 1 | Viewed by 2479

Abstract

Low levels of physical activity (PA) and sleep disruption are commonly seen in older adult inpatients and are associated with poor health outcomes. Wearable sensors allow for objective continuous monitoring; however, there is no consensus as to how wearable sensors should be implemented. This review aimed to provide an overview of the use of wearable sensors in older adult inpatient populations, including models used, body placement and outcome measures. Five databases were searched; 89 articles met inclusion criteria. We found that studies used heterogenous methods, including a variety of sensor models, placement and outcome measures. Most studies reported the use of only one sensor, with either the wrist or thigh being the preferred location in PA studies and the wrist for sleep outcomes. The reported PA measures can be mostly characterised as the frequency and duration of PA (Volume) with fewer measures relating to intensity (rate of magnitude) and pattern of activity (distribution per day/week). Sleep and circadian rhythm measures were reported less frequently with a limited number of studies providing both physical activity and sleep/circadian rhythm outcomes concurrently. This review provides recommendations for future research in older adult inpatient populations. With protocols of best practice, wearable sensors could facilitate the monitoring of inpatient recovery and provide measures to inform participant stratification and establish common objective endpoints across clinical trials. Full article

(This article belongs to the Special Issue Wearable Sensor Technologies for Physiological and Environmental Monitoring)

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