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。 Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.
7. Jeong J, Cho W, Kim Y, et al. Recognition of lower limb muscle EMG patterns by using neural networks during the postural balance control//3rd Kuala Lumpur International Conference On Biomedical Engineering 2006, Berlin: Springer, 2007: 82-85.
15. Preece S J, Goulermas J Y, Kenney L P, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 2009, 56(3): 871-879.
17. Abhayasinghe N, Murray I. Human activity recognition using thigh angle derived from single thigh mounted IMU data//2014 International Conference On Indoor Positioning And Indoor Navigation (IPIN), IEEE, 2014: 111-115.
13. Kim S K, Hong S, Kim D. A walking motion imitation framework of a humanoid robot by human walking recognition from IMU motion data//IEEE-Ras International Conference on Humanoid Robots, IEEE, 2009:343-348.
21. You Y, Qian Y, He T, et al. An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition//IEEE China Summit and International Conference on Signal and Information Processing, IEEE, 2015: 30-34.
23. Zhou X, Zhou C, Stewart B G. Comparisons of discrete wavelet transform, wavelet packet transform and stationary wavelet transform in denoising PD measurement data//Conference Record of the 2006 IEEE International Symposium on Electrical Insulation, IEEE, 2006: 237-240.