Li Hao
1. Beijing Research Institute Precision Mechatronics and Controls, Beijing 100076, China; 2. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076, ChinaYu Zhiyuan
1. Beijing Research Institute Precision Mechatronics and Controls, Beijing 100076, China; 2. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076, ChinaYin Yecheng
1. Beijing Research Institute Precision Mechatronics and Controls, Beijing 100076, China; 2. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076, ChinaYan Guodong
1. Beijing Research Institute Precision Mechatronics and Controls, Beijing 100076, China; 2. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076, China1. Beijing Research Institute Precision Mechatronics and Controls, Beijing 100076, China; 2. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076, China
TP391.4
With the development of sensor technology and microelectronics, it has a wide application value to recognize human motion patterns by wearable sensors. It is of great significance to improve the accuracy of recognition. This paper considered the characteristics of human lower limb motion, and proposed a human motion pattern recognition algorithm based on CNN and Mogrifier LSTM. First, CNN is used to extract local related features of the original data, then Mogrifier LSTM is used to replace the full connection layer to mine the front and back dependencies of local related features. Recognize the six common motion patterns of walking, running, upstairs, downstairs, uphill and downhill. The experimental results show that compared with the traditional LSTM algorithm, the accuracy of Mogrifier LSTM is improved by 1.03%. After combining CNN and Mogrifier LSTM, the accuracy is further improved by 1.17% to 98.18%, which proves the superiority of the algorithm proposed in this paper.
