Qiu Jia-Gang, Li Yi, Liu Hao-Qi, Lin Shuang, Pang Lei, Sun Gang, Song Ying-Zhe
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
Math Biosci Eng. 2023 Jul 5;20(8):14578-14595. doi: 10.3934/mbe.2023652.
Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.
运动识别为身体功能障碍者、老年人以及运动感应游戏制作提供运动信息，对于准确识别人类运动非常重要。我们采用了三种经典机器学习算法和三种深度学习算法模型进行运动识别，即随机森林（RF）、K近邻（KNN）和决策树（DT）以及动态神经网络（DNN）、卷积神经网络（CNN）和循环神经网络（RNN）。与佩戴在身体七个部位的惯性测量单元（IMU）进行比较。总体而言，本研究中三种经典机器学习算法之间的性能差异不显著。RF算法模型表现最佳，识别率达到96.67%，其次是KNN算法模型，最佳识别率为95.31%，DT算法的最佳识别率为94.85%。深度学习算法模型之间的性能差异显著。DNN算法模型表现最佳，识别率达到97.71%。我们的研究验证了使用多维数据进行运动识别的可行性，并表明基于多维传感数据区分日常活动的最佳佩戴部位是腰部。在算法方面，基于多维传感器的深度学习算法表现更好，而树结构模型在传统机器学习算法中仍具有较好的性能。结果表明，IMU与深度学习算法相结合可以有效地识别动作，并为运动识别领域更广泛的应用提供了有前景的基础。
