Literature DB >> 33302770

Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review.

Amanpreet Kaur1.   

Abstract

The human-machine interface (HMI) and bio-signals have been used to control rehabilitation equipment and improve the lives of people with severe disabilities. This research depicts a review of electromyogram (EMG) or electrooculogram (EOG) signal-based control system for driving the wheelchair for disabled. For a paralysed person, EOG is one of the most useful signals that help to successfully communicate with the environment by using eye movements. In the case of amputation, the selection of muscles according to the distribution of power and frequency highly contributes to the specific motion of a wheelchair. Taking into account the day-to-day activities of persons with disabilities, both technologies are being used to design EMG or EOG based wheelchairs. This review paper examines a total of 70 EMG studies and 25 EOG studies published from 2000 to 2019. In addition, this paper covers current technologies used in wheelchair systems for signal capture, filtering, characterisation, and classification, including control commands such as left and right turns, forward and reverse motion, acceleration, deceleration, and wheelchair stop.

Entities:  

Keywords:  Electromyogram (EMG); electrooculography (EOG); human machine interface; wheelchair

Year:  2020        PMID: 33302770     DOI: 10.1080/03091902.2020.1853838

Source DB:  PubMed          Journal:  J Med Eng Technol        ISSN: 0309-1902


  3 in total

1.  Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Authors:  Quan Zhang; Tao Jin; Jianguo Cai; Liang Xu; Tianyiyi He; Tianhong Wang; Yingzhong Tian; Long Li; Yan Peng; Chengkuo Lee
Journal:  Adv Sci (Weinh)       Date:  2021-11-19       Impact factor: 16.806

2.  Evaluating surface EMG control of motorized wheelchairs for amyotrophic lateral sclerosis patients.

Authors:  Albert C Manero; Shea L McLinden; John Sparkman; Björn Oskarsson
Journal:  J Neuroeng Rehabil       Date:  2022-08-14       Impact factor: 5.208

3.  Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback.

Authors:  Bo Zhu; Daohui Zhang; Yaqi Chu; Xingang Zhao; Lixin Zhang; Lina Zhao
Journal:  Front Neurorobot       Date:  2021-07-16       Impact factor: 2.650

  3 in total

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