Wrist-Worn Hand Gesture Recognition based on Barometric Pressure Sensing

Abstract

Hand gestures are expressive motions that convey meaningful information. The ability for machines to extract and process the underlying meanings of these gestures is critical to many human-interactive applications. Various methods have been proposed, but the development of a more accurate, and simpler system could enable the machine and its user to exchange useful information more effectively. In this paper, a barometric-pressure-sensor-based wristband is presented as an initial proof of such concept. The wristband is composed of an array of 10 barometric pressure sensors spaced evenly around the wrist to estimate pressure profiles as tendons and muscles change with various hand gestures. Subject testing was performed to quantify classification accuracy for three groups of hand gestures: group 1) six wrist gestures, group 2) five single finger flexions, and group 3) ten Chinese number gestures. Leave-one-out cross-validation was used to compute classification accuracy. Results demonstrated classification accuracies of 98% for the wrist gestures, 95% for the single finger flexions, and 90% for Chinese number gestures. The presented pressure sensing wristband could potentially be used for a variety of applications including gesture-controlled devices, health-monitoring devices, and assistive devices for deaf-mute individuals.