This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.
Real-Time Human Body Posture Estimation Using Neural Networks.