Spatio-Temporally Optimized Multi-sensor Motion Fusion

Abstract

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The latest advances in smart sensor technology, e.g., Leap Motion Sensor, has increased the precision in tracking fully articulated human hand and finger movements, without the need for placing electrical or optical markers. A remaining challenge is finger occlusion, which can affect tracking accuracy. In this paper, we introduce a spatio-temporal optimization technique for motion data generated from multiple sensors. We demonstrate that our algorithm can produce a fused stream of probabilistic optimal hand poses, by improving local spatial domain analysis and proposing a fast and effective flow analysis technique in the temporal domain, which computes how well the hand pose estimation in the current frame fits the movement flow within a time segment. By using an artificial hand to represent the hand pose ground truth at selected time steps, experimental results demonstrate that our spatio-temporal optimization algorithm increases the estimation accuracy by 6% compared to the reference method, achieving an overall accuracy of 91.29%. Our proposed method can be used offline or in real-time, and can benefit a wide range of applications, including surgical planning and training, where hand motion is the focus of performance efficiency and assessment.