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IoT-Based Wearable Sensors and Health and Performance Monitoring in the Home and Hospital-Based Rehabilitation

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1187

Special Issue Editors

Dr. Yeon Sik Noh

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Nursing, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: biomedical instrumentation; biosignal sensors and electrodes; wearable devices; smart and connected health; Internet of Things; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Sungjae Hwang

E-Mail Website
Guest Editor
Department of Kinesiology, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
Interests: biomechanics; kinesiology; cerebral palsy; brain injury; sensorimotor function; gait; postural control; balance; rehabilitation

Special Issue Information

Dear Colleagues,

Digital health technologies , such as smartwatches, smartphones, and smart biometric sensors that are worn on the body, and machine and deep learning-based data analytics have become widely accepted in home- and hospital-based rehabilitation programs for health and performance monitoring during therapeutic exercises. By taking advantage of these advanced digital health technologies, recent efforts have been made to create various self-monitoring applications and ensure adherence to home-/remote/hospital-based rehabilitation programs and personalized and patient-centered therapeutic exercise protocols.

Therefore, this Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of rehabilitation engineering research.

The potential topics include, but are not limited to:

  • Internet of Things (IoT)-based health monitoring;
  • Wearable sensors for electrophysiological and motor monitoring;
  • Home and hospital-based rehabilitation settings;
  • Remote and mobile rehabilitation;
  • Aquatic therapy and rehabilitation;
  • Digital health technology;
  • Technology for adherence to and effectiveness of home/remote/hospital therapeutic exercise;
  • User-/patient-centered rehabilitation programs and protocols;
  • Computer-assisted rehabilitation tools;
  • Exoskeleton;
  • Posture and balance.

Dr. Yeon Sik Noh
Dr. Sungjae Hwang
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 devices
  • smart health diagnostics
  • digital health
  • flexible sensors
  • sensory motor system
  • machine and deep learning
  • signal processing
  • computer-assisted rehabilitation
  • exoskeleton
  • exercise science

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Published Papers (1 paper)

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Research

20 pages, 3876 KiB  
Article
An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder
by Dundi Umamaheswara Reddy, Kanaparthi V. Phani Kumar, Bandaru Ramakrishna and Ganapathy Sankar Umaiorubagam
Sensors 2024, 24(22), 7184; https://doi.org/10.3390/s24227184 - 9 Nov 2024
Viewed by 395
Abstract
Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and [...] Read more.
Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis. Full article
(This article belongs to the Special Issue IoT-Based Wearable Sensors and Health and Performance Monitoring in the Home and Hospital-Based Rehabilitation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Digital health technologies , such as smartwatches, smartphones, and smart biometric sensors that are worn on the body, and machine and deep learning-based data analytics have become widely accepted in home- and hospital-based rehabilitation programs for health and performance monitoring during therapeutic exercises. By taking advantage of these advanced digital health technologies, recent efforts have been made to create various self-monitoring applications and ensure adherence to home-/remote/hospital-based rehabilitation programs and personalized and patient-centered therapeutic exercise protocols.

Therefore, this Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of rehabilitation engineering research.

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