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.

Background: In the fields of rehabilitation, public health, military training and other domains, the accurate and effective monitoring of exercise intensity during exercise can control the occurrence of sports injuries, which is of great significance for people’s healthy lives. Objective: This study combined easily collectable multi-dimensional sensor data and various algorithm models to achieve cross-individual recognition of low, middle and high levels of exercise intensity. Methods: This study compared the recognition performance of different algorithm models using acceleration and angular velocity sensors worn on seven body parts through individualised body data characteristics. Results: The recognition performances of two classical machine learning algorithms were the worst, with a recognition rate of only 82.97% and 80.31%. The performances of two ensemble learning algorithms were slightly better, with a recognition rate of 88.86% and 87.35%. The deep sub-domain adaptation network algorithm proposed in this study exhibited the best performance, with a recognition rate of 92.87%. This study utilised multi-dimensional sensors to construct a cross-individual exercise intensity recognition model for different parts of the body, and the overall recognition rate of the left part was higher than that of the right part. Moreover, the recognition effect upon wearing sensors on the left side of the body is better than the right in running events. Conclusions: The results of this study have demonstrated the effectiveness of combining domain adaptation methods and multi-dimensional sensors for cross-individual exercise intensity recognition, laying a solid theoretical foundation for broader-scale cross-individual exercise intensity recognition in future research.