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Wearable Sensing of Medical Condition at Home Environment
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Wearable Sensing of Medical Condition at Home Environment

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3549

Special Issue Editors

Dr. Gaddi Blumrosen

E-Mail Website
Guest Editor
1. Department of Digital Medical Technologies, Holon Institute of Technology, Holon 5810201, Israel
2. Department of Computer Science, Holon Institute of Technology, Holon 5810201, Israel
Interests: biomedical engineering; machine learning; neuroscience; digital signal processing; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Amos Bardea

E-Mail Website
Guest Editor
Department of Electrical Engineering, Holon Institute of Technology, Holon 5810201, Israel
Interests: bioelectronics; bio- and chemosensors; nanopatterning; magnetic materials; advanced materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, wearable technology has become a major tool for the continuous assessment of patients’ medical condition in their home environment. This is enabled by recent technological advances in machine learning techniques, as well as seamless computationally efficient and miniature sensors with more reliable connectivity. Currently, there are growing efforts to adopt better wearable technology to enhance medical condition monitoring and assessment in home and community settings, design new sensors, tailor sensors with advanced machine learning techniques for advanced diagnostics, and to establish clinical decision-support infrastructure.

The goal of this Special Issue is to publish the most up-to-date research in the field of wearables to enable the monitoring and diagnostics of medical condition for the elderly, in particular to assess subject cognitive functionality and impairment over time in real-life environments while performing daily life activities. This includes brain signals, heart rate, respiratory rate, blood pressure, blood oxygen saturation, and recordings of neural and muscle activity.

This Special Issue covers advances in sensor data analysis technologies, sensor aggregation, medical decision-support systems, the development of comprehensive sensors, and medical wearable technology system design, with the goal of enabling the remote monitoring of individuals in home and community settings.

Dr. Gaddi Blumrosen
Dr. Amos Bardea
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

  • wearable sensing
  • home monitoring
  • health monitoring
  • healthcare
  • sensor signal processing
  • diagnostics

Published Papers (2 papers)

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Research

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14 pages, 2057 KiB  
Article
Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning
by Hagar Gelbard-Sagiv, Snir Pardo, Nir Getter, Miriam Guendelman, Felix Benninger, Dror Kraus, Oren Shriki and Shay Ben-Sasson
Sensors 2023, 23(13), 5805; https://doi.org/10.3390/s23135805 - 21 Jun 2023
Cited by 1 | Viewed by 1840
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing [...] Read more.
Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection. Full article
(This article belongs to the Special Issue Wearable Sensing of Medical Condition at Home Environment)
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Review

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16 pages, 2705 KiB  
Review
Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review
by Ekta Singh Dahiya, Anubha Manju Kalra, Andrew Lowe and Gautam Anand
Sensors 2024, 24(4), 1318; https://doi.org/10.3390/s24041318 - 18 Feb 2024
Cited by 1 | Viewed by 1194
Abstract
In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, [...] Read more.
In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions. Full article
(This article belongs to the Special Issue Wearable Sensing of Medical Condition at Home Environment)
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