惯性动捕数据驱动下的智能下肢假肢运动意图识别方法

为了解决传统意图识别方法使用多模态传感器信号所带来的复杂性以及识别转换模式一般具有滞后性等问题, 本文提出了基于惯性传感器的智能下肢假肢的运动意图实时识别方法.从模式识别的角度看, 在对象空间到模式空间的转换中, 对运动模式尤其是运动转换模式进行了重定义; 在模式采集中, 采用在患侧的运动模式进行转换之前, 采集绑定在健侧的传感器于摆动相前期所产生的时序运动数据, 选择均值、方差等特征统计量和支持向量机分类器对其进行特征选择提取与特征分类的策略, 实现对残疾人运动意图准确、实时地识别.实验结果表明, 本文所提出的方法可以识别出单肢截肢患者在不同地形下的运动意图, 包括平地行走、上楼、下楼、上坡、下坡5种稳态模式, 识别率可达到97.52 %, 并且加入在5种模式之间相互转换的转换模式之后, 识别率可达到95.12 %.本文方法可以极大提高智能下肢假肢的控制性能, 实现智能假肢能根据人的运动意图在多种运动模式之间进行自然、无缝的状态切换.

An Improved Motion Intent Recognition Method for Intelligent Lower Limb Prosthesis Driven by Inertial Motion Capture Data

In order to overcome the drawbacks of conventional intention recognition methods, including complexities associated with multi-modal sensor signals and lags of transitional state recognition, this paper proposes a real-time motion intent recognition method for intelligent lower limb prosthesis base on inertial sensors. From the perspective of pattern recognition, the motion patterns, especially the motion transformation patterns, are redeflned in the transformation from object space to pattern space. In pattern acquisition, our strategy is that, prior to the movement mode conversion of lower limb prosthesis, motion time-series data generated by the sensors bound to the contralateral side during the early swing phase are collected, and the corresponding statistical features such as mean and variance are extracted. Feature classiflcation is performed by using a support vector machine classifler to achieve the accurate, real-time identiflcation for motion intent of the disabled movement with intelligent lower limb prosthesis. Our proposal is able to recognize various motion intents containing 5 steady states as well as 8 transitional states among the steady states on difierent terrains including level ground, stair ascent, stair descent, ramp ascent and ramp descent. Experimental results show the recognition accuracy can reach at 97.52 % and 95.12 % on those patterns from steady states and transitional states, respectively. The proposed method can greatly improve the control performance of intelligent lower limb prostheses, and can achieve the natural and seamless state switch of the intelligent prosthesis movement according to the intention of the human movement.