Biotech, Biomaterials and Biomedical: TechConnect Briefs 2016Biotech, Biomaterials and Biomedical TechConnect Briefs 2016

Sensors, Diagnostics & Imaging Chapter 4

Research on human-computer interaction technology based on electrical detection technology

H-P. Han, K. Tang, X-Q. Lang, X. Chen
Beijing Institute Technology, China

pp. 144 - 147

Keywords: electrostatic detection, gesture recognition, human-computer interaction

Human-computer interaction (HCI) technology has emerged as a crucial dimension of computer science and information industry. HCI with keyboard and mouse has started to lose ground, as people tend to harness the power of body language to facilitate HCI and computer operation, which promises to render non-contact HCI easier and more natural. This paper sheds light on a proposed approach to non-contact HCI based on human body static electricity where detection-electrode arrays are aligned properly to track and recognize hand motion. First, based on the charging mechanism of hand motion, we follow the theorem of electrostatic induction to examine how current signals change as sensed by detection electrodes when a hand moves in parallel with a metal plate. As a result, we establish the correlation between hand motion dynamics and electric current changes. Next, we transforms the weak current into voltage signals to substantiate the position-based correlation between the trajectory of hand motion and the wave pattern of voltage signals. The finding indicates that voltage wave patterns may signify the direction of hand motion. Third, we designs multiple electrodes and aligns them properly so that different wave profiles are produced when electrodes are spaced and positioned differently. Finally, we employs the method of moving filter to facilitate wave pattern analysis. We designs algorithm for phase detection so as to determine the time of extreme points and zero-crossing time of wave shapes on each electrode. The wave pattern findings are then used to recognize gestures. Numerous experiments are conducted to determine the feasibility and the success rate of the proposed hand tracking and recognition system. In this paper, we design a hand tracking and recognition system that proves that non-contact HCI can be made possible by using hand motion parameters gained from examining electrostatic induction signals that are produced on metal plates when hands are in motion.