As we known, time synchronization is a crucial component of wireless sensor networks (WSNs), especially when performing the operation such as data fusion. In this paper, we revisit this problem for our proposed wireless motion capture (MoCap) nodes, namely WonderSense (WS), and present an easy and economical way to compensate time drift. We first explain the work principles of the proposed nodes. This part includes the overview of the device, and the approach to compensate the time drift for the built MoCap system. Then, the extended Kalman filter (EKF) for attitude estimation is implemented and briefly described. At last, several experiments are conducted to verify the feasibility of the time drift compensation method and validate the time drift compensated MoCap system during human walking. Results indicate that, on one hand, the proposed time drift compensation method is feasible, on the other hand, it is proved that the time drift compensated MoCap system is viable and reliable.

It is important for many applications to capture hand movements with high accuracy to achieve the natural human-computer interaction, such as games, robotics, rehabilitation, and virtual reality (VR). An ideal hand motion capture solution requires good mobility, unobtrusiveness, and high accuracy. In this demo, we show a hand motion capture system including inertial sensor based data gloves with the square-root cubature Kalman Filter multi-sensor fusion algorithm and a biomechanics sensor-to-segment calibration method. The absolute error of the joint angle is measured. As the result, the proposed system shows good accuracy in both static (RMSE = 1.5°) and dynamic (RMSE = 6.6°) conditions.