Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation.
Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation.
复制标题
基于人在环优化的神经康复参与增强
DOI：
10.3389/fnbot.2020.596019
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发表时间：
2020
影响因子：
3.1
通讯作者：
Hou ZG
中科院分区：
文献类型：
--
作者：
Wang J;Wang W;Ren S;Shi W;Hou ZG
提高患者的参与度对神经康复大有裨益。然而，个体之间的生理和神经差异会导致对同一任务的不同反应，而且在训练过程中反应可能会发生很大变化；这两个因素都使提高参与度成为一项挑战。这一挑战可以通过基于受试者反应的训练任务优化来克服。为此，本文提出了一种基于人在环优化的参与度增强方法。首先，设计了一个交互式速度跟踪骑行游戏作为训练任务，设计了四条参考速度曲线(RSC)来构建每代人的参考轨迹。每个RSC使用分段函数来建模，该分段函数由起始速度、瞬时时间和结束速度确定。基于该参数化模型，可以对影响交战的关键因素--训练任务的难度进行优化。然后，综合考虑跟踪精度和基于表面肌电信号(SEMG)的肌肉激活来设计目标函数，从而可以同时评价受试者的生理和生理反应。此外，采用一种对测量噪声和人类自适应都具有相对耐受性的协方差矩阵自适应进化策略来周期性地生成RSC的最优参数。通过对RSC的持续优化，可以最大化目标函数，提高受试者的参与度。最后，通过验证和对比实验，验证了该方法的有效性。结果表明，两组受试者基于表面肌电信号的运动投入度和基于脑电的神经投入度均有显著提高，并维持在较高水平。
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.
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影响因子：
3
作者：
Arns, Martijn;Conners, C. Keith;Kraemer, Helena C.
通讯作者：
Kraemer, Helena C.
DOI：
10.1109/tnsre.2011.2160357
发表时间：
2011-08-01
影响因子：
4.9
作者：
Novak, Domen;Mihelj, Matjaz;Munih, Marko
通讯作者：
Munih, Marko
影响因子：
5.2
作者：
Agarwal, Priyanshu;Deshpande, Ashish D.
通讯作者：
Deshpande, Ashish D.
影响因子：
2.7
作者：
Dunn, DS;Dougherty, SB
通讯作者：
Dougherty, SB
影响因子：
5.7
作者：
Loo, Sandra K.;Makeig, Scott
通讯作者：
Makeig, Scott