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Wearable Sensors for Behavioral and Physiological Monitoring
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Wearable Sensors for Behavioral and Physiological Monitoring

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2361

Special Issue Editors

Prof. Dr. Joan Condell

E-Mail Website
Guest Editor
School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK
Interests: wearables; IoT; big data; health analytics; innovation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gerard Leavey

E-Mail Website
Co-Guest Editor
Director, The Bamford Centre, Ulster University, Coleraine, BT52 1SA, UK
Interests: mental health services; loneliness; religion and spirituality; epidemiology; administrative data sets; qualitative research

Special Issue Information

Dear Colleagues,

Wearable technologies offer a promising opportunity to empower individuals to not only manage their own health but to explore trends and actively engage with primary health services. Wearable technologies have significant market penetration (e.g., 12% of UK adults regularly use a wearable device). Despite their low penetration compared to mobile phones, wearable devices have much lower attrition compared to mobile apps for health, thus maximising their potential impact.

There is growing pressure, post-COVID-19, on various health services globally; waiting lists continue to rise and demand for primary and secondary care services continues to increase. While innovative technologies have the potential to elevate some of this burden and empower individuals at home, the true cost of such technologies must be explored. Where such pressure exists both on individuals and the global health system at large, we must focus on sustainable health; wearables offer an ideal platform, empowering individuals and connecting care in the home to the primary care setting.

Prof. Dr. Joan Condell
Prof. Dr. Gerard Leavey
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • wearables
  • sustainable health
  • remote monitoring
  • wearable sustainability

Published Papers (2 papers)

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Research

19 pages, 7191 KiB  
Article
Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition
by Heba Nematallah and Sreeraman Rajan
Sensors 2024, 24(7), 2119; https://doi.org/10.3390/s24072119 - 26 Mar 2024
Viewed by 457
Abstract
Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR [...] Read more.
Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity’s sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition. Full article
(This article belongs to the Special Issue Wearable Sensors for Behavioral and Physiological Monitoring)
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15 pages, 1767 KiB  
Article
Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors
by Yewei Ouyang, Ming Liu, Cheng Cheng, Yuchen Yang, Shiyi He and Lan Zheng
Sensors 2023, 23(17), 7405; https://doi.org/10.3390/s23177405 - 25 Aug 2023
Viewed by 1442
Abstract
Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers’ inattention caused by physical fatigue utilizing electrocardiograph (ECG) [...] Read more.
Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers’ inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers’ inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers’ physiological features and attention. Full article
(This article belongs to the Special Issue Wearable Sensors for Behavioral and Physiological Monitoring)
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