The utilization of upper extremity exoskeleton robots has been proved to be a scientifically effective approach for rehabilitation training. In the process of rehabilitation training, it is necessary to detect the fatigue degree during rehabilitation training in order to formulate a reasonable training plan and achieve better training efficiency. Based on the integral value of surface electromyography (sEMG), heart rate variability, and instantaneous heart rate, this article proposes a fatigue judgment method for multi-information fusion. Based on the integral value data, the feature extraction of the bioelectrical signals were implemented separately, then the fatigue recognition was conducted using the decision-level data fusion method. The bioelectrical signal acquisition system of electromyogram signals and electrocardiograph signals was developed for upper limb exoskeleton rehabilitation robot, and the acquisition and processing of electromyogram signals and electrocardiograph signals were completed. Finally, the fuzzy logic controller with instantaneous heart rate, heart rate variability, and surface electromyography signal was designed to judge fatigue degree, including the fuzzy device, fuzzy rule selector, and defuzzifier. The moderate fatigue state data were selected for testing, and the experimental results showed that the error of fatigue judgment is 4.3%, which satisfies the requirements of fatigue judgment.

中文翻译：

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基于多信息融合的上肢外骨骼机器人康复训练疲劳检​​测新方法

上肢外骨骼机器人的使用已被证明是一种科学有效的康复训练方法。在康复训练过程中，需要对康复训练过程中的疲劳程度进行检测，以便制定合理的训练计划，达到更好的训练效率。本文基于表面肌电图（sEMG）积分值、心率变异性和瞬时心率，提出一种多信息融合的疲劳判断方法。基于积分值数据，分别对生物电信号进行特征提取，然后采用决策级数据融合方法进行疲劳识别。为上肢外骨骼康复机器人开发了肌电信号和心电图信号生物电信号采集系统，完成了肌电信号和心电图信号的采集和处理。最后，设计了具有瞬时心率、心率变异性和表面肌电信号的模糊逻辑控制器来判断疲劳程度，包括模糊装置、模糊规则选择器和去模糊器。选取中等疲劳状态数据进行测试，实验结果表明疲劳判断误差为4.3%，满足疲劳判断要求。最后，设计了具有瞬时心率、心率变异性和表面肌电信号的模糊逻辑控制器来判断疲劳程度，包括模糊装置、模糊规则选择器和去模糊器。选取中等疲劳状态数据进行测试，实验结果表明疲劳判断误差为4.3%，满足疲劳判断要求。最后，设计了具有瞬时心率、心率变异性和表面肌电信号的模糊逻辑控制器来判断疲劳程度，包括模糊装置、模糊规则选择器和去模糊器。选取中等疲劳状态数据进行测试，实验结果表明疲劳判断误差为4.3%，满足疲劳判断要求。