RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems
One of the most difficult parts of stroke therapy is hand mobility recovery. Indeed, stroke is a serious medical disorder that can seriously impair hand and locomotor movement. To improve hand function in stroke patients, new medical technologies, such as various wearable devices and rehabilitation...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/12/4/95 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823860400742989824 |
|---|---|
| author | Ismail Ben Abdallah Yassine Bouteraa |
| author_facet | Ismail Ben Abdallah Yassine Bouteraa |
| author_sort | Ismail Ben Abdallah |
| collection | DOAJ |
| description | One of the most difficult parts of stroke therapy is hand mobility recovery. Indeed, stroke is a serious medical disorder that can seriously impair hand and locomotor movement. To improve hand function in stroke patients, new medical technologies, such as various wearable devices and rehabilitation therapies, are being developed. In this study, a new design of electromyography (EMG)-controlled 3D-printed hand exoskeleton is presented. The exoskeleton was created to help stroke victims with their gripping abilities. Computer-aided design software was used to create the device’s 3D architecture, which was then printed using a polylactic acid filament. For online classifications, the performance of two classifiers—the support vector machine (SVM) and the K-near neighbor (KNN)—was compared. The Robot Operating System (ROS) connects all the various system nodes and generates the decision for the hand exoskeleton. The selected classifiers had high accuracy, reaching up to 98% for online classification performed with healthy subjects. These findings imply that the new wearable exoskeleton, which could be controlled in accordance with the subjects’ motion intentions, could aid in hand rehabilitation for a wider motion range and greater dexterity. |
| format | Article |
| id | doaj-art-143695bc4b1647df9a72f59961744504 |
| institution | Kabale University |
| issn | 2218-6581 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj-art-143695bc4b1647df9a72f599617445042025-02-10T14:06:38ZengMDPI AGRobotics2218-65812023-07-011249510.3390/robotics12040095RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded SystemsIsmail Ben Abdallah0Yassine Bouteraa1Control and Energy Management Laboratory (CEM Lab.), Ecole Nationale d’Ingénieurs de Sfax (ENIS), University of Sfax, Sfax 3038, TunisiaDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaOne of the most difficult parts of stroke therapy is hand mobility recovery. Indeed, stroke is a serious medical disorder that can seriously impair hand and locomotor movement. To improve hand function in stroke patients, new medical technologies, such as various wearable devices and rehabilitation therapies, are being developed. In this study, a new design of electromyography (EMG)-controlled 3D-printed hand exoskeleton is presented. The exoskeleton was created to help stroke victims with their gripping abilities. Computer-aided design software was used to create the device’s 3D architecture, which was then printed using a polylactic acid filament. For online classifications, the performance of two classifiers—the support vector machine (SVM) and the K-near neighbor (KNN)—was compared. The Robot Operating System (ROS) connects all the various system nodes and generates the decision for the hand exoskeleton. The selected classifiers had high accuracy, reaching up to 98% for online classification performed with healthy subjects. These findings imply that the new wearable exoskeleton, which could be controlled in accordance with the subjects’ motion intentions, could aid in hand rehabilitation for a wider motion range and greater dexterity.https://www.mdpi.com/2218-6581/12/4/95robotic hand exoskeletonsEMGfeatures extractionRobot Operating SystemSVM classifierKNN classifier |
| spellingShingle | Ismail Ben Abdallah Yassine Bouteraa RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems Robotics robotic hand exoskeleton sEMG features extraction Robot Operating System SVM classifier KNN classifier |
| title | RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems |
| title_full | RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems |
| title_fullStr | RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems |
| title_full_unstemmed | RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems |
| title_short | RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems |
| title_sort | retracted a newly designed wearable robotic hand exoskeleton controlled by emg signals and ros embedded systems |
| topic | robotic hand exoskeleton sEMG features extraction Robot Operating System SVM classifier KNN classifier |
| url | https://www.mdpi.com/2218-6581/12/4/95 |
| work_keys_str_mv | AT ismailbenabdallah retractedanewlydesignedwearablerobotichandexoskeletoncontrolledbyemgsignalsandrosembeddedsystems AT yassinebouteraa retractedanewlydesignedwearablerobotichandexoskeletoncontrolledbyemgsignalsandrosembeddedsystems |