Biomechanical model-based multi-sensor motion estimation
Sensors Applications Symposium(2013)
摘要
Motion estimation drift has been a challenge in inertial sensor motion capture research. This paper presents a novel biomechanical model-based multi-sensor motion estimation method working on a group of sensor units attached to a limb. In this method, biomechanical model provides constraints and defines relationships among sensors. The motion parameters of neighboring segments are estimated together by using unscented Kalman filter with those constraints and relationships. The performance of this method is benchmarked through the optical/inertial combined capture experiments. The experiment results show that our algorithm increases the accuracy of motion estimation.
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关键词
biomechanics,unscented kalman filter,kalman filters,multi-sensor data fusion,multisensor motion estimation,biomechanical model,medical signal processing,inertial sensor motion capture research,motion estimation,motion capture,limb,imu,motion estimation drift,sensor fusion
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