Hand shape classification with a wrist contour sensor: development of a prototype device

In this paper, we describe a novel sensor device which recognizes hand shapes using wrist contours. Although hand shapes can express various meanings with small gestures, utilization of hand shapes as an interface is rare in domestic use. That is because a concise recognition method has not been established. To recognize hand shapes anywhere with no stress on the user, we developed a wearable wrist contour sensor device and a recognition system. In the system, features, such as sum of gaps, were extracted from wrist contours. We conducted a classification test of eight hand shapes, and realized approximately 70% classification rate.

[1]  Jun Rekimoto,et al.  GestureWrist and GesturePad: unobtrusive wearable interaction devices , 2001, Proceedings Fifth International Symposium on Wearable Computers.

[2]  Mircea Nicolescu,et al.  A Review on Vision-Based Full DOF Hand Motion Estimation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[3]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[4]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Vladimir Pavlovic,et al.  Hand Gesture Modeling, Analysis, and Synthesis , 1995 .

[6]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[7]  K. Nagata,et al.  A Classification Method of Hand Movements Using Multi Channel Electrode , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.