人体动作和路况的快速准确识别是实现智能假肢自主控制的基础与前提。本文提出了一种基于假肢（下肢）惯导信号的高斯混合模型（GMM）和隐马尔可夫模型（HMM）融合的人体动作和路况识别方法。首先，使用惯性传感器采集膝关节处 x、y 和 z 轴方向上的加速度、角度和角速度信号，然后用时间窗截取信号段并用小波包变换消除信号的抖动噪声；接着对预处理后的信号进行快速傅里叶变换，提取其系数作为特征值；随后对特征进行主成分分析（PCA），去除冗余信息；最后采用高斯混合模型和隐马尔可夫模型进行假肢动作和路况识别。试验结果表明，本文方法对常规的动作（散步、跑步、骑行、上坡、下坡、上楼梯和下楼梯）的识别率分别达到 96.25%、92.5%、96.25%、91.25%、93.75%、88.75% 和 90%。同等试验条件下，将本文方法与常规的支持向量机（SVM）识别方法进行比较，结果显示本文方法的识别率明显较高。本文研究结果或可为智能假肢的监测和控制提供新的思路和途径
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