Schweisfurth Meike A, Ernst Jennifer, Vujaklija Ivan, Schilling Arndt F, Farina Dario, Aszmann Oskar C, Felmerer Gunther
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1-6. doi: 10.1109/ICORR.2017.8009212.
Targeted muscle reinnervation (TMR) represents a breakthrough interface for prosthetic control in high-level upper-limb amputees. However, clinically, it is still limited to the direct motion-wise control restricted by the number of reinnervation sites. Pattern recognition may overcome this limitation. Previous studies on EMG classification in TMR patients experienced with myocontrol have shown greater accuracy when using high-density (HD) recordings compared to conventional single-channel derivations. This case study investigates the potential of HD-EMG classification longitudinally over a period of 17 months post-surgery in a glenohumeral amputee. Five experimental sessions, separated by approximately 3 months, were performed. They were timed during a standard rehabilitation protocol that included intensive physio- and occupational therapy, myosignal training, and routine use of the final myoprosthesis. The EMG signals recorded by HD-EMG grids were classified into 12 classes. The first sign of EMG activity was observed in the second experimental session. The classification accuracy over 12 classes was 76% in the third session and ∼95% in the last two sessions. When using training and testing sets that were acquired with a 1-h time interval in between, a much lower accuracy (32%, Session 4) was obtained, which improved upon prosthesis usage (Session 5, 67%). The results document the improvement in EMG classification accuracy throughout the TMR-rehabilitation process.
靶向肌肉再支配（TMR）是高水平上肢截肢者假肢控制方面的一项突破性技术。然而，在临床上，它仍局限于受再支配部位数量限制的直接运动控制。模式识别可能会克服这一限制。先前针对有肌电控制经验的TMR患者进行的肌电图分类研究表明，与传统单通道记录相比，使用高密度（HD）记录时准确性更高。本案例研究调查了一名盂肱关节截肢患者术后17个月内纵向进行高密度肌电图分类的潜力。共进行了五次实验，每次间隔约3个月。实验时间安排在标准康复方案期间，该方案包括强化物理治疗和职业治疗、肌电信号训练以及最终肌电假肢的常规使用。通过高密度肌电图网格记录的肌电信号被分为12类。在第二次实验中观察到了肌电活动的首个迹象。在第三次实验中，12类信号的分类准确率为76%，在最后两次实验中约为95%。当使用间隔1小时采集的训练集和测试集时，准确率要低得多（32%，第四次实验），而在假肢使用时准确率有所提高（第五次实验，67%）。研究结果证明了在整个TMR康复过程中肌电图分类准确率的提高。
