Innovative Practice of Sensor Fusion Technology and Intelligent Algorithm in Fitness Effect Evaluation

With the development of science and technology, fitness has evolved into a scientific, systematic and personalized lifestyle. Firstly, this paper introduces the basic principle and fusion strategy of sensor fusion technology, especially the multi-sensor information fusion strategy based on Kalman filter (KF), in order to improve the accuracy and reliability of fitness effect evaluation. Then, the article elaborates the application of intelligent algorithm in personalized fitness planning, sports risk assessment, and sports attitude assessment and feedback. Through deep learning recommendation system, support vector machine (SVM) and OpenPose algorithm, in-depth analysis and real-time feedback of user fitness data are realized. In the experimental design part, 30 volunteers with different fitness levels were selected, and the data were collected by high-definition cameras and various sensors, which verified the effectiveness of the proposed method. The experimental results show that the deep learning recommendation system can generate personalized fitness plan, the SVM algorithm can accurately predict the sports risk, and the OpenPose algorithm can combine the sensor data to feedback the sports posture in real time. KF's multi-sensor information fusion strategy effectively improves the accuracy of data, and the application of intelligent algorithm improves the scientificity and efficiency of fitness. Although there are limitations, this study provides new ideas and methods for improving the accuracy and personalized level of fitness effect evaluation in the future.