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基于贝叶斯优化的康复训练参与度自适应增强方法研究

曾洪 陈晴晴 李潇 张建喜 宋爱国

曾洪, 陈晴晴, 李潇, 张建喜, 宋爱国. 基于贝叶斯优化的康复训练参与度自适应增强方法研究[J]. 电子与信息学报, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122
引用本文: 曾洪, 陈晴晴, 李潇, 张建喜, 宋爱国. 基于贝叶斯优化的康复训练参与度自适应增强方法研究[J]. 电子与信息学报, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122
ZENG Hong, CHEN Qingqing, LI Xiao, ZHANG Jianxi, SONG Aiguo. Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122
Citation: ZENG Hong, CHEN Qingqing, LI Xiao, ZHANG Jianxi, SONG Aiguo. Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122

基于贝叶斯优化的康复训练参与度自适应增强方法研究

doi: 10.11999/JEIT221122
基金项目: 国家自然科学基金(62173089)
详细信息
    作者简介:

    曾洪:男,博士,副教授,研究方向为神经信号-机器人接口与交互技术、力触觉接口与交互技术、脑机混合智能技术

    陈晴晴:女,硕士生,康复机器人、人机交互、自适应控制

    李潇:女,博士生,研究方向为康复机器人,神经-肌肉控制、肌肉协同

    张建喜:男,博士生,研究方向为人机交互、外肢体机器人

    宋爱国:男,博士,教授,研究方向为空间机器人遥操作技术、脑-机接口与脑机融合技术、医疗机器人与康复机器人技术

    通讯作者:

    陈晴晴 qingqingchen620@163.com

  • 中图分类号: TP242; TH-39

Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization

Funds: The National Natural Science Foundation of China (62173089)
  • 摘要: 针对现有的评估被试主动参与度指标建模复杂以及训练强度与被试运动能力及参与度不匹配等问题,该文提出一种基于贝叶斯优化的挑战型力控制器自适应增强康复训练参与度的方法。首先使用基于表面肌电信号(sEMG)表征的肌肉激活度来评估被试者的参与度,其次采用基于轨迹误差放大的抗阻训练模式进行上肢训练,并构建归一化急动度和肌肉激活度相结合的综合目标函数,然后采用贝叶斯优化方法在训练过程中更新挑战型力场的抗阻系数和死区宽度两个超参数,逐次优化该目标函数,以提高运动轨迹的顺滑度并保持被试者的训练参与度。最后,将16名健康被试者随机分为实验组和对照组并以其非利手进行训练,验证所提方法的有效性。实验结果表明,训练过程中实验组的肌肉激活度维持在高于对照组2.51%的水平;训练后实验组的运动能力改善明显优于对照组(59.8% vs 40.7%),验证了该文所提的自适应增强康复训练参与度策略比固定参数策略更有优势。
  • 图  1  训练过程中的一个实验场景

    图  2  自适应增强康复训练参与度的超参数优化策略

    图  3  伪随机选取贝叶斯优化的初始值

    图  4  贝叶斯优化过程图

    图  5  训练过程中指标的变化

    图  6  多重比较检验

    表  1  训练过程安排

    参数过程1过程2过程3过程4过程5过程6过程7
    时间(min)53533035
    实验阶段熟悉休息训练前休息训练中休息训练后
    实验次数(次)50506+1405
    下载: 导出CSV

    表  2  归一化急动度多重比较检验

    多重比较检验均值差95%置信区间P
    Pre_G对比Post_G7.613[5.834 , 9.392]<0.0001
    Pre_G对比Pre_Y0.461[–1.318 , 2.240]0.8790
    Pre_G对比Post_Y10.840[9.061 , 12.620]<0.0001
    Post_G对比Pre_Y–7.152[–8.931 , –5.373]<0.0001
    Post_G对比Post_Y3.228[1.449 , 5.007]0.0005
    Pre_Y对比Post_Y10.380[8.600 , 12.160]<0.0001
    下载: 导出CSV

    表  3  肌肉激活度多重比较检验

    多重比较检验均值差95%置信区间P
    Pre_G对比Post_G–0.009177[–0.02105, 0.00270]0.1625
    Pre_G对比Pre_Y–0.003366[–0.01524, 0.00851]0.8485
    Pre_G对比Post_Y–0.035720[–0.04760, –0.02385]<0.0001
    Post_G对比Pre_Y0.005812[–0.00607, 0.01769]0.5172
    Post_G对比Post_Y–0.026550[–0.03842, –0.01467]<0.0001
    Pre_Y对比Post_Y–0.032360[–0.04423, –0.02048]<0.0001
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-29
  • 修回日期:  2022-10-30
  • 网络出版日期:  2022-11-05
  • 刊出日期:  2023-08-21

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