Adaptive Information Fusion for Human Upper Limb Movement Estimation

Accurate human movement estimation techniques are widely used in various applications, such as robotics, human-machine interaction, sports, and rehabilitation. With rapid advances in microsensors, human movement estimation using wearable micro inertial sensors has become an active research topic. The main challenges for the wearable sensor motion estimation are the inertial sensor drift problem and the linear acceleration interference problem. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion estimation. In this paper, we take the upper limb as our research subject and present a novel upper limb movement estimation algorithm to cope with these two challenges by adaptive fusion of sensor data and human skeleton constraint. In the sensor fusion part, a quaternion-based unscented Kalman filter is invoked to fuse the gyroscope, accelerometer, and magnetometer measurement information. In the Kalman filter framework, an acceleration interference detection scheme is implemented based on the exponentially discounted average of the normalized innovation squared (NIS). According to the detection results, the process and measurement noise levels are scaled up or down automatically. To further compensate for the drift, we present a novel solution by modeling geometrical constraint in the elbow joint and fuse the constraint to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results in comparison to the BTS SMART-D optical motion tracker.