一种下肢运动意图识别算法性能实时测评系统

Real-time performance test evaluation system for lower limb motion intention recognition algorithm

对腿部运动意图识别算法的实时性能进行综合可靠的评价是实现下肢假肢灵活稳定控制的前提。提出一种逐层分级的基于下肢运动意图识别算法的实时测评方法,对算法的可靠性、稳定性以及运动意图识别速度进行综合评价。利用开发的运动意图识别算法评测系统,对基于肌电信号源和机械信号源的两种运动意图识别算法进行了实时性能测试,发现肌电信号源的算法识别时间大于机械信号源算法,但是其算法稳定性优于机械信号源算法。进一步地,还利用该评测系统有效地区分出正常识别策略与异常识别策略,发现正常策略的动作识别稳定系数比异常策略高25%左右。因此,所提的基于下肢运动意图识别算法的实时测评方法,能够对不同信号源算法以及不同识别策略的实时性能进行有效评价,为智能下肢假肢控制系统开发提供可供参考的测试平台。

Comprehensive and reliable evaluation of the real-time performance of leg motion intention recognition algorithms is the premise to realize flexible and stable control of lower limb prosthesis.In this paper,a multi-layer real time test evaluation method for lower limb motion intention recognition algorithms is proposed,which comprehensively evaluate the reliability,stability and motion intention recognition speed of the algorithms.Using the developed test evaluation system for lower limb motion intention recognition algorithms,two motion intention recognition algorithms based on myoelectric signal source and mechanical signal source were tested in real time,respectively.The results show that the motion recognition time of the myoelectric signal source based algorithm is longer than that of the mechanical signal source based algorithm;however,the stability of the myoelectric signal source based algorithm is better than that of the mechanical signal source based algorithm.Additionally,the performances of normal and abnormal recognition strategies can be effectively distinguished using the proposed test evaluation system,and it is found that the motion recognition stability index for normal strategy is 25%higher than that for the abnormal strategy.These results demonstrate that the proposed real time test evaluation method for lower limb motion intention recognition algorithms can effectively evaluate the real time performance of different signal source based algorithms and different recognition strategies,and can provide a testing platform for the development of intelligent lower limb prostheses control system.