Zhang Fan, Dou Zhi, Nunnery Michael, Huang He
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2997-3000. doi: 10.1109/IEMBS.2011.6090822.
This paper presents a real-time implementation of an intent recognition system on one transfemoral (TF) amputee. Surface Electromyographic (EMG) signals recorded from residual thigh muscles and the ground reaction forces/moments collected from the prosthetic pylon were fused to identify three locomotion modes (level-ground walking, stair ascent, and stair descent) and tasks such as sitting and standing. The designed system based on neuromuscular-mechanical fusion can accurately identify the performing tasks and predict intended task transitions of the patient with a TF amputation in real-time. The overall recognition accuracy in static states (i.e. the states when subjects continuously performed the same task) was 98.36%. All task transitions were correctly recognized 80-323 ms before the defined critical timing for safe switch of prosthesis control mode. These promising results indicate the potential of designed intent recognition system for neural control of computerized, powered prosthetic legs.
本文介绍了一种在一名经股（TF）截肢者身上实时实现的意图识别系统。从残肢大腿肌肉记录的表面肌电图（EMG）信号与从假肢支柱收集的地面反作用力/力矩进行融合，以识别三种运动模式（平地行走、上楼梯和下楼梯）以及诸如坐和站等任务。基于神经肌肉-机械融合设计的系统能够实时准确识别患者执行的任务，并预测经股截肢患者预期的任务转换。静态状态（即受试者持续执行相同任务的状态）下的总体识别准确率为98.36%。在定义的假肢控制模式安全切换关键时间之前80 - 323毫秒，所有任务转换均被正确识别。这些令人鼓舞的结果表明了所设计的意图识别系统用于计算机化动力假肢神经控制的潜力。
