シナジーを活用した低コストグローブによる手姿勢推定の高精度化
Synergy-based Hand Pose Sensing: Reconstruction Enhancement
低コストなセンシンググローブの計測データに、人間の手姿勢の統計的分布（ポスチャルシナジー）を事前情報として融合し、不十分でノイズの多い計測でも高精度に手姿勢を再構成する手法を提案した。
著者: Matteo Bianchi, Paolo Salaris, Antonio Bicchi
分類: cs.RO
原文アブストラクト
Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the 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.
