谷歌浏览器插件
订阅小程序
在清言上使用

Interactive Virtual Ankle Movement Controlled by Wrist Semg Improves Motor Imagery: an Exploratory Study.

Yanqing Xiao, Hongming Bai,Yang Gao,Ben Hu, Jia Zheng, Xiaoe Cai,Jiasheng Rao,Xiaoguang Li,Aimin Hao

IEEE transactions on visualization and computer graphics(2024)

引用 0|浏览11
暂无评分
摘要
Virtual reality (VR) techniques can significantly enhance motor imagery training by creating a strong illusion of action for central sensory stimulation. In this article, we establish a precedent by using surface electromyography (sEMG) of contralateral wrist movement to trigger virtual ankle movement through an improved data-driven approach with a continuous sEMG signal for fast and accurate intention recognition. Our developed VR interactive system can provide feedback training for stroke patients in the early stages, even if there is no active ankle movement. Our objectives are to evaluate: 1) the effects of VR immersion mode on body illusion, kinesthetic illusion, and motor imagery performance in stroke patients; 2) the effects of motivation and attention when utilizing wrist sEMG as a trigger signal for virtual ankle motion; 3) the acute effects on motor function in stroke patients. Through a series of well-designed experiments, we have found that, compared to the 2D condition, VR significantly increases the degree of kinesthetic illusion and body ownership of the patients, and improves their motor imagery performance and motor memory. When compared to conditions without feedback, using contralateral wrist sEMG signals as trigger signals for virtual ankle movement enhances patients' sustained attention and motivation during repetitive tasks. Furthermore, the combination of VR and feedback has an acute impact on motor function. Our exploratory study suggests that the sEMG-based immersive virtual interactive feedback provides an effective option for active rehabilitation training for severe hemiplegia patients in the early stages, with great potential for clinical application.
更多
查看译文
关键词
Training,Stroke (medical condition),Wrist,Muscles,Legged locomotion,Biological system modeling,Real-time systems,VR-based stroke rehabilitation training,motor imagery,sEMG-based virtual feedback
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要