In robotic rehabilitation, the classification of motion intents and detection of fatigue from surface electromyography (sEMG) are important to guarantee safety during the rehabilitation process. The time-varying characteristics of sEMG can induce errors in related applications such as force/torque estimation, detection of muscle fatigue, and pattern recognition. We investigated the effects of long-term wrist contractions on the classification accuracy of stroke patients in fatigue. Seven stroke patients participated to repeatedly perform sessions of four isometric wrist movements, namely, flexion, extension, radial deviation, and ulnar deviation in different sessions until exhaustion over 4 days. Each movement was successively performed by 60 s with 30 s of rest. To avoid excessive muscle fatigue, subjects were asked to perform each movement at 20% of the maximum voluntary contraction. We classified the four types of wrist movements using an artificial neural network and investigated variations of sEMG features in fatigue. The results showed that not only the classification accuracy but also the manifestation of muscle fatigue from sEMG remained consistent during long-term contractions in fatigue. The average classification accuracy for all patients was 0.91 ± 0.07 without significant difference between sessions.

在机器人康复中，运动意图的分类和表面肌电图（sEMG）检测疲劳对于确保康复过程中的安全性很重要。sEMG的时变特性会在相关应用中引起错误，例如力/扭矩估计，肌肉疲劳检测和模式识别。我们调查了长期腕部收缩对中风患者疲劳分类准确度的影响。七名中风患者参加了重复四次等距腕部运动的练习，即在不同的练习中进行屈曲，伸展，radial骨偏移和尺骨偏移，直到4天疲惫为止。每次运动连续60秒，休息30秒。为了避免过度的肌肉疲劳，要求受试者以最大自愿收缩量的20％进行每次运动。我们使用人工神经网络对四种类型的腕部运动进行了分类，并研究了疲劳中sEMG功能的变化。结果表明，在长期疲劳收缩过程中，不仅分类准确度高，而且sEMG引起的肌肉疲劳表现也保持一致。所有患者的平均分类准确度为0.91±0.07，两次治疗之间无显着差异。结果表明，在长期疲劳收缩过程中，不仅分类准确度高，而且sEMG引起的肌肉疲劳表现也保持一致。所有患者的平均分类准确度为0.91±0.07，两次治疗之间无显着差异。结果表明，在长期疲劳收缩中，不仅分类准确度而且sEMG引起的肌肉疲劳表现也保持一致。所有患者的平均分类准确度为0.91±0.07，各次检查之间无显着差异。