Synergy-based hand pose sensing: Reconstruction enhancement

Many applications in human-machine interfaces, information visualization, rehabilitation and entertainment are calling for hand pose reconstruction systems that are both accurate and economic. Unfortunately, economically and ergonomically viable sensing gloves provide limited precision due to imperfect and incomplete correspondence of sensing models with the anatomical degrees-of-freedom of the human hand, and because of measurement noise. This paper examines the problem of optimally estimating the posture of a human hand using non-ideal sensing gloves. The main idea is to maximize their performance by exploiting knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware — hence basically at no extra cost — we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low–cost glove are reported which demonstrate the effectiveness of the proposed techniques.