Papers
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Total Citations
26
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About
Dr. Gary To is a leading researcher in sensor fusion, attitude estimation, and human motion tracking, with a particular focus on quaternion-based methods for robotic and wearable systems. His most cited work, "Quaternionic Attitude Estimation for Robotic and Human Motion Tracking Using Sequential Monte Carlo Methods With von Mises-Fisher and Nonuniform Densities Simulations" (2013, 26 citations), introduced a novel framework that leverages von Mises-Fisher distributions within particle filters to achieve robust orientation tracking from MEMS sensor data. This contribution addressed critical challenges in handling non-Gaussian noise and multimodal uncertainties, advancing the reliability of inertial measurement units in consumer electronics and robotics. Dr. To’s research has directly influenced the development of more accurate and computationally efficient algorithms for real-time motion capture, with applications spanning from human-computer interaction to autonomous navigation. His work is widely recognized for bridging theoretical advances in Bayesian filtering with practical sensor fusion solutions, making him a key figure in the field of motion estimation and wearable technology.
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