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Wearable Assistive Rehabilitation Robotic Devices—A Comprehensive Review

Authors :
Pavan Kalyan Lingampally
Kuppan Chetty Ramanathan
Ragavanantham Shanmugam
Lenka Cepova
Sachin Salunkhe
Source :
Machines, Vol 12, Iss 6, p 415 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This article details the existing wearable assistive devices that could mimic a human’s active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process.

Details

Language :
English
ISSN :
20751702
Volume :
12
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
edsdoj.7e69add69a54b84a003f1fc456361b0
Document Type :
article
Full Text :
https://doi.org/10.3390/machines12060415