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Special Issue "Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare"

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

Deadline for manuscript submissions: 1 March 2021.

Special Issue Editors

Prof. Dr. Se Dong Min
Website
Guest Editor
Soonchunhyang University, Asan, Korea
Interests: smart health; wearable healthcare sensors; mobile healthcare; flexible sensors for health monitoring
Dr. Ki H. Chon
Website
Guest Editor
Biomedical Engineering Department, University of Connecticut, Storrs, CT, USA
Interests: biomedical sensors; biomedical signal processing; medical instrumentation; deep learning; machine learning; heart rate variability; physiological monitoring; cardiovascular arrhythmia detection; atrial fibrillation detection; wearable devices; photoplethysmographic sensors; electrodermal activity; fatigue; pain detection

Special Issue Information

Dear Colleagues,

Due to the recent ongoing pandemic, personal hygiene control and healthcare in everyday life are emerging as important factors in terms of preventive medicine. Under these circumstances, wearable and remote sensors and devices have recently come into the spotlight as a solution, due to their capacity to continuously and easily measure and monitor individual health status. In addition, it is predicted that the methods of health promotion activities, such as medical treatment and rehabilitation, will switch to being non-face-to-face, in consideration of pandemics. Given these points, this Special Issue will present a selection of related topics, including healthcare methods utilizing new technologies such as wearable sensors, remote sensors, and AR/VR devices in terms of personal healthcare, diagnosis, and rehabilitation methods.

This Special Issue aims to explore the opportunities and challenges regarding the application of sensor technologies for the measurement, monitoring, and diagnostics of individual health within the context of a new concept of healthcare.

Contributions that address the following topics, in addition to any other related topics, are welcome:

  • Wearable Healthcare Sensors and Systems;
  • Remote Healthcare Solutions;
  • New Concept of Telemedicine System;
  • Flexible Sensors for Healthcare and Diagnostics;
  • AR/VR-Based Rehabilitation Systems;
  • Remote Vital Sign Measurement Systems;
  • Mobile Devices for Diagnostics;
  • Smart Healthcare System.

Prof. Dr. Se Dong Min
Prof. Dr. Dr. Ki H. Chon
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 papers will be 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 2200 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
  • Remote sensors
  • Telemedicine
  • Flexible sensors
  • Mobile devices
  • AR/VR
  • Vital signs
  • Rehabilitation
  • Smart healthcare
  • Long-term healthcare

Published Papers (4 papers)

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Research

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Open AccessArticle
Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation
Sensors 2021, 21(4), 1537; https://doi.org/10.3390/s21041537 - 23 Feb 2021
Abstract
The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient’s motivation and [...] Read more.
The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient’s motivation and reducing the therapist’s work. The paper presents a VR simulator for an intelligent robotic system of physiotherapeutic rehabilitation of the ankle of a person who has had a stroke. This simulator can interact with a real human subject by attaching a sensor that contains a gyroscope and accelerometer to identify the position and acceleration of foot movement on three axes. An electromyography (EMG) sensor is also attached to the patient’s leg muscles to measure muscle activity because a patient who is in a worse condition has weaker muscle activity. The data collected from the sensors are taken by an intelligent module that uses machine learning to create new levels of exercise and control of the robotic rehabilitation structure of the virtual environment. Starting from these objectives, the virtual reality simulator created will have a low dependence on the therapist, this being the main improvement over other simulators already created for this purpose. Full article
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Review

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Open AccessReview
Near-Field Communication in Biomedical Applications
Sensors 2021, 21(3), 703; https://doi.org/10.3390/s21030703 - 20 Jan 2021
Abstract
Near-field communication (NFC) is a low-power wireless communication technology used in contemporary daily life. This technology contributes not only to user identification and payment methods, but also to various biomedical fields such as healthcare and disease monitoring. This paper focuses on biomedical applications [...] Read more.
Near-field communication (NFC) is a low-power wireless communication technology used in contemporary daily life. This technology contributes not only to user identification and payment methods, but also to various biomedical fields such as healthcare and disease monitoring. This paper focuses on biomedical applications among the diverse applications of NFC. It addresses the benefits of combining traditional and new sensors (temperature, pressure, electrophysiology, blood flow, sweat, etc.) with NFC technology. Specifically, this report describes how NFC technology, which is simply applied in everyday life, can be combined with sensors to present vision and opportunities to modern people. Full article
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Other

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Open AccessLetter
Principal Characteristics of Affected and Unaffected Side Trunk Movement and Gait Event Parameters during Hemiplegic Stroke Gait with IMU Sensor
Sensors 2020, 20(24), 7338; https://doi.org/10.3390/s20247338 - 21 Dec 2020
Abstract
This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify [...] Read more.
This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify the affected, unaffected, and healthy lower-limb sides; (2) Methods: The study monitored forty patients with stroke and twenty-eight healthy individuals comprising the control group for comparison. The IMU sensors were attached to each subject while performing a 6 m walking test. Sixteen variables extracted from the measured data were divided into 7 GE and 9 TM variables explaining pelvis tilt, oblique, and rotation. (3) Results: The tilt range variables of the trunk movement on the affected and unaffected sides were lower than those of the healthy side; this showed between-group differences in various GE variables. For the healthy and affected sides, 80% of variances were explained with 2 or 3 PCs involving only a few dominant variables; and (4) Conclusions: The difference between each side leg should be considered during the development of a diagnosis method. This research can be utilized to develop functional assessment tools for personalized treatment and to design appropriate training protocols. Full article
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Open AccessLetter
Development of a Diagnosis and Evaluation System for Hemiplegic Patients Post-Stroke Based on Motion Recognition Tracking and Analysis of Wrist Joint Kinematics
Sensors 2020, 20(16), 4548; https://doi.org/10.3390/s20164548 - 13 Aug 2020
Abstract
An inexperienced therapist lacks the analysis of a patient’s movement. In addition, the patient does not receive objective feedback from the therapist due to the visual subjective judgment. The aim is to provide a guide for in-depth rehabilitation therapy in virtual space by [...] Read more.
An inexperienced therapist lacks the analysis of a patient’s movement. In addition, the patient does not receive objective feedback from the therapist due to the visual subjective judgment. The aim is to provide a guide for in-depth rehabilitation therapy in virtual space by continuously tracking the user’s wrist joint during Leap Motion Controller (LMC) activities and present the basic data to confirm steady therapy results in real-time. The conventional Box and Block Test (BBT) is commonly used in upper extremity rehabilitation therapy. It was modeled in proportion to the actual size and Auto Desk Inventor was used to perform the 3D modeling work. The created 3D object was then implemented in C # through Unity5.6.2p4 based on LMC. After obtaining a wrist joint motion value, the motion was analyzed by 3D graph. Healthy subjects (23 males and 25 females, n = 48) were enrolled in this study. There was no statistically significant counting difference between conventional BBT and system BBT. This indicates the possibility of effective diagnosis and evaluation of hemiplegic patients post-stroke. We can keep track of wrist joints, check real-time continuous feedback in the implemented virtual space, and provide the basic data for an LMC-based quantitative rehabilitation therapy guide. Full article
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