Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69225
Title: Adaptive sensor data fusion in motion capture
Authors: Sun, S.
Meng, X.
Ji, L.
Wu, J.
Wong, W.-C. 
Keywords: Adaptive weighting
Drift
Human motion capture
Kalman filtering
Sensor fusion
Issue Date: 2010
Source: Sun, S.,Meng, X.,Ji, L.,Wu, J.,Wong, W.-C. (2010). Adaptive sensor data fusion in motion capture. 13th Conference on Information Fusion, Fusion 2010 : -. ScholarBank@NUS Repository.
Abstract: Micro-sensor human motion capture has shown its potentials because of its ubiquity and low cost. One of the biggest challenges in micro-sensor motion estimation is the drift problem caused by integration of angular rates to obtain orientation. To reduce the drift, existing algorithms make use of gravity and earth magnetic filed measured by accelerometers and magnetometers respectively. Unfortunately, body segment acceleration and environment magnetic disturbance produce strong interferences to the gravity and earth magnetic field measurement respectively. This paper presents a novel sensor fusion algorithm for driftfree orientation estimation, where a quaternion-based complementary Kalman filter is designed. To optimize the performance under interference, this filter fuses gyroscope, accelerometer and magnetometer signals adaptively based on their information confidence, which are evaluated by computing their interference level. The proposed algorithm showed least error compared with the existing methods in the quantitative experiments, and its effectiveness was also verified by the stable and accurate human motion estimation.
Source Title: 13th Conference on Information Fusion, Fusion 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/69225
ISBN: 9780982443811
Appears in Collections:Staff Publications

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