Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation
Wang, Jiaxing1,2; Wang, Weiqun2; Ren, Shixin1,2; Shi, Weiguo1,2; Hou, Zeng-Guang1,2,3
发表期刊FRONTIERS IN NEUROROBOTICS
ISSN1662-5218
2020-11-12
卷号12期号:页码:11
摘要

Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

关键词human-in-the-loop optimization EEG based neural engagement sEMG based muscle activation tracking accuracy neural rehabilitation
DOI10.3389/fnbot.2020.596019
关键词[WOS]EEG ; ATTENTION ; ASSISTANCE ; DISORDER ; PARTICIPATION ; HYPERACTIVITY ; PERFORMANCE ; ADAPTATION ; WORK
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[91848110] ; National Key R&D Program of China[2018YFB1307804] ; Beijing Natural Science Foundation[4202074] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000]
项目资助者National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Science
WOS研究方向Computer Science ; Robotics ; Neurosciences & Neurology
WOS类目Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS记录号WOS:000592233300001
出版者FRONTIERS MEDIA SA
七大方向——子方向分类多模态智能
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/41777
专题复杂系统管理与控制国家重点实验室_先进机器人
通讯作者Wang, Weiqun
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang, Jiaxing,Wang, Weiqun,Ren, Shixin,et al. Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation[J]. FRONTIERS IN NEUROROBOTICS,2020,12(无):11.
APA Wang, Jiaxing,Wang, Weiqun,Ren, Shixin,Shi, Weiguo,&Hou, Zeng-Guang.(2020).Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation.FRONTIERS IN NEUROROBOTICS,12(无),11.
MLA Wang, Jiaxing,et al."Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation".FRONTIERS IN NEUROROBOTICS 12.无(2020):11.
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