A data fusion-based method for detecting difficult yoga postures

Abstract

The current traditional human pose detection methods mainly acquire the pose data collected by sensors and realize the pose discrimination by edge computing decision unit, which leads to poor detection effect due to the lack of fusion of sensor data. In this regard, a high difficulty yoga body pose detection method based on data fusion is proposed. Complementary filtering, Kalman filtering and adaptive Kalman filtering methods are used to fuse sensor data for pose prediction, and a skeleton extraction model is constructed to build a multilayer perceptron network architecture to achieve the detection of difficult yoga asana poses. In the experiments, the proposed human posture detection method is validated. The analysis of the experimental results shows that the proposed method has a high comprehensive evaluation index and excellent detection performance when used for the detection of difficult yoga asana postures.