Hand Motion Measurement using Inertial Sensor System and Accurate Improvement by Extended Kalman Filter

Analysis of hand motions is crucial in such actual conditions as daily life and traditional work. We developed a measurement system using inertial sensors instead of an optical motion capture system that measures with spatial constraints. However, for these sensors, the posture error caused by the integration of the angular velocity is critical. A typical solution uses sensor fusion with simple observation equations to measure such lower limbs by walking analysis. For finger motions, a simple observation, calculated identically as the initial posture, is unsuitable because fingers may be moved intricately and quickly by multiple joints and parallel links. Therefore, we constructed an observation equation based on such dynamic acceleration as rotational acceleration and the correction of compass error. Using this suggested observation equation, since both the posture and position error were verified in the hand and forearm motions by a comparison with the optical motion capture, we could measure them with high accuracy. After measuring the movements of an actual hand, such as writing words and spinning a top, we analyzed the characteristics from a reproduced link model and joint angles.