Sensor Data Fusion and Motion Status Monitoring in Smart Wearable Devices

In view of the limitations of traditional motion monitoring methods in data accuracy and real-time performance, this study introduces multi-sensor data fusion technology to improve the accuracy and reliability of motion state monitoring. By fusing the data of accelerometer, gyroscope and heart rate sensor in real time, the user's motion state can be analyzed more comprehensively, providing more accurate health analysis and feedback. First, this paper designs a sensor data fusion framework based on Kalman filtering, which fuses the data of accelerometer, gyroscope and heart rate sensor. Through the Kalman filtering algorithm, this paper can reduce the impact of sensor error on motion state detection while ensuring computational efficiency. Secondly, the support vector machine (SVM) is used to classify and analyze the fused data, and the user's motion type (such as gait, running, cycling, etc.) is extracted based on time series features. Finally, in order to verify the effectiveness of the algorithm, this paper collects sensor data of different users in different motion states and evaluates the performance of the system in multiple scenarios. In a static environment, the recognition accuracy of walking and running is generally high, with the walking recognition rate of user 1 being 96.54% and the running recognition rate being 94.17%. The walking recognition rate of user 2 reaches 98.31%, and the running recognition rate reaches 95.04%. This study proposes a new motion state monitoring method through multi-sensor data fusion technology, which significantly improves the accuracy and real-time performance of motion monitoring. This method provides strong technical support for the application of smart wearable devices in health management and sports tracking.