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Wearable Sensing Technologies for Human Health Monitoring
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Wearable Sensing Technologies for Human Health Monitoring

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2523

Special Issue Editor

Dr. Jayraj V. Vaghasiya

E-Mail Website
Guest Editor
Future Energy and Innovation Laboratory, Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
Interests: flexible electronics; sensors; 2D materials; telehealth; human–machine interface; flexible energy devices; 3D printing

Special Issue Information

Dear Colleagues,

In an era dominated by technological innovation, wearable devices are revolutionizing healthcare by providing real-time insights into various aspects of human health. From the continuous monitoring of vital signs to tracking physical activity and sleep patterns, these technologies offer unprecedented opportunities for personalized and proactive healthcare.

This Special Issue aligns seamlessly with the scope of “Sensors”, exploring the intersection of sensor technologies and human health monitoring. Sensors play a pivotal role in the functionality of wearable devices, capturing and translating physiological data into actionable insights. This Special Issue will encompass a diverse range of sensor types, including but not limited to biosensors, pressure sensors, strain sensors, electrochemical sensors, and optoelectronic sensors, highlighting their integration into wearable platforms. By focusing on wearable sensing technologies, this Special Issue will contribute to the ongoing discourse on sensor applications in healthcare, promoting interdisciplinary research at the crossroads of sensor technology, data science, and healthcare innovation.

Dr. Jayraj V. Vaghasiya
Guest Editor

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
  • telehealth
  • health monitoring
  • remote patient monitoring
  • biometric data
  • internet of things
  • biomedical engineering
  • digital health
  • smart textiles
  • data analytics
  • personalized healthcare

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

16 pages, 3806 KiB  
Article
2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data
by Hongzhe Zhang, Jihui L. Diaz, Soohyun Kim, Zilong Yu, Yiyuan Wu, Emily Carter and Samprit Banerjee
Sensors 2024, 24(21), 7053; https://doi.org/10.3390/s24217053 - 31 Oct 2024
Viewed by 766
Abstract
Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across [...] Read more.
Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated. Full article
(This article belongs to the Special Issue Wearable Sensing Technologies for Human Health Monitoring)
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20 pages, 1793 KiB  
Article
ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration
by Raghavendra Ganiga, Muralikrishna S. N., Wooyeol Choi and Sungbum Pan
Sensors 2024, 24(10), 3140; https://doi.org/10.3390/s24103140 - 15 May 2024
Cited by 1 | Viewed by 1248
Abstract
Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, [...] Read more.
Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual’s identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information. Full article
(This article belongs to the Special Issue Wearable Sensing Technologies for Human Health Monitoring)
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