基于CNN-Mogrifier LSTM的人体运动模式识别算法

随着传感器、微电子等技术的发展，通过可穿戴式传感器对人体的运动模式进行识别，具有广泛的应用价值，如何提高识别的准确率，具有重要研究意义。考虑到人体下肢运动的特点，本文提出了一种基于CNN和Mogrifier LSTM的人体运动模式识别算法，先利用CNN提取原始数据的局部相关特征，再使用Mogrifier LSTM代替全连接层，挖掘局部相关特征的前后依赖关系，对行走、跑步、上楼梯、下楼梯、上坡和下坡六种常见的运动模式进行识别。实验结果表明，相比于传统LSTM算法，Mogrifier LSTM的准确率提升了1.03%，将CNN和Mogrifier LSTM相结合后，准确率进一步提升了1.17%，达到了98.18%，证明了算法的优越性。

Human motion pattern recognition algorithm based on CNN-Mogrifier LSTM

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.