Optimized per-joint compression of hand motion data

Motion data is quickly expanding its application scope, following the recent advancements in smart sensing technology. In particular, it has been shown to be helpful for objective measurement and assessment of surgical dexterity among users at different levels of training. The goal is to allow trainees to evaluate their performance based on a reference set of hand movements. Similar to other multimedia data types, recording motion can produce a substantial amount of data, some of which are redundant for the application. Compression methods aim to optimize storage and transmission of motion capture (MoCap) data by taking advantage of temporal and spatial correlation. Hand motion data is a special sub-type of MoCap and is the focus of many applications, where hand movement evaluation is important. In this paper, we propose a lossy but visually indifferent, compression method that exploits redundancy found in hand motion data. Since individual joint movements have different impacts on the motion sequence, our technique is designed to minimize the overall distortion by providing a per-joint compression. We are able to demonstrate that our approach offers a quantitative gain for different compression ratios, while preserving visual quality.