Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization
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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。Abstract: For the problems that the existing evaluation index of patient active engagement is complicated to model and training intensity does not match the exercise ability and participation of the participants. A challenge force controller based on Bayesian optimization to enhance adaptively the participation of rehabilitation training is proposed. Firstly, muscle activation based on surface ElectroMyoGram (sEMG) signal is used to evaluate the participant's participation. Secondly, the resistance training mode based on trajectory error amplification is used to train the upper limb, and a comprehensive objective function combining normalized intensity and muscle activation is constructed. Then, Bayesian optimization method is used to update the resistance coefficient and dead zone width of the challenge force field in each training, and optimize the objective function continuously to improve the smoothness of the motion trajectory, while maintaining the participants' participation in training. Finally, 16 healthy subjects are randomly divided into experimental group and control group and trained with their non-handedness to verify the effectiveness of the proposed method. The experimental results show that the muscle activation of the experimental group is 2.51% higher than that of the control group. After training, the improvement of exercise ability in the experimental group is significantly better than that in the control group (59.8% vs 40.7%), which verifies that the adaptive rehabilitation training engagement strategy proposed has more advantages than the fixed parameter strategy.
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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值 Pre_G对比Post_G 7.613 [5.834 , 9.392] <0.0001 Pre_G对比Pre_Y 0.461 [–1.318 , 2.240] 0.8790 Pre_G对比Post_Y 10.840 [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_Y 3.228 [1.449 , 5.007] 0.0005 Pre_Y对比Post_Y 10.380 [8.600 , 12.160] <0.0001 
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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值 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_Y 0.005812 [–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
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