Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm quaternion method are selected after analysing a variety commonly used data fusion pose solving algorithms. sensor-body coordinate system calibration based on hand-eye is proposed, which only requires three poses to complete whole-body system. joint parameter estimation human constraints limb length closed chains respectively. The iterative optimization that divides each iteration into expectation step great likelihood step, best convergence value can be found efficiently according step. feature values action fed for model learning, enables training model. trained then tested by combining collected gesture with algorithmic recognize classify data, observe its recognition accuracy, continuously optimize achieve accurate actions.