Real-time implementation of an intent recognition system for artificial legs.
Real-time implementation of an intent recognition system for artificial legs.
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实时实施人造腿的意图识别系统。
DOI：
10.1109/iembs.2011.6090822
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Huang H
中科院分区：
文献类型：
--
作者：
Zhang F;Dou Z;Nunnery M;Huang H
本文提出了一个实时实现的意图识别系统对一个经股（TF）截肢者。表面肌电图（EMG）信号记录从剩余的大腿肌肉和地面反作用力/力矩收集的假肢pylon融合，以确定三种运动模式（平地行走，楼梯上升，楼梯下降）和任务，如坐和站。所设计的基于神经肌肉-机械融合的系统可以准确地识别执行任务，并实时预测TF截肢患者的预期任务转换。静态（即受试者连续执行相同任务的状态）的总体识别准确率为98.36%。所有任务转换在定义的假体控制模式安全切换的关键时间之前80-323 ms被正确识别。这些有希望的结果表明，潜在的意图识别系统的神经控制的计算机化，动力假肢。
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.