Real-time tracking of the effect of jumping rope exercise using a wearable device

With the development of science and technology, wearable devices, as an emerging field, have been gradually integrated into our daily lives and are widely used in the tracking of movement effects. In this paper, the data fusion algorithm combining complementary filtering and extended Kalman filtering and the human posture solving algorithm based on the D-H method is selected to solve the designed human jumping rope motion joint model, which realizes the construction of a wearable jumping rope motion capture system. Furthermore, the effect and commercial value of the wearable device designed in this paper for real-time tracking of jumping rope movement are tested by a single node posture test and a comparison experiment with posture solving. The experimental results show that the static test error and dynamic test accuracy of the sensor are 1.4° and 4°, respectively, which indicate that the sensor can accurately recognize the trajectory of jumping rope movements. The average values of RMSE for pitch angle, roll angle, and yaw angle were 0.37, 0.69, and 1.40, respectively. This indicates that the wearable device and the pose-solving algorithm used in this paper can meet the standard for commercial applications. This study provides a new approach to studying sports, which has rarely been done in the field of smart sports.