Design of a flexible data glove for gesture recognition

As a newly emerged assistive device, data gloves are able to help amputees rebuild their sense of haptic perception, empower robots with dexterous manipulation, and even enhance human’s capability of remote sensing and control. Up to now, it has boosted a wide range of potential applications, e.g., tele-operation, medical rehabilitation, and virtual reality. In this paper, a low-cost and easy-to-fabricate flexible data glove was designed consisting of 5–row by 4–column piezo-resistive sensing elements (SEs), two flexible electronic circuits and two protective polydimethylsiloxane (PDMS) layers. Based on the design of a ’switch’ structure and a simplified wiring layout, the stimulus locations and force values could be determined conveniently with a dynamic scanning algorithm, although there were only 9-path signal outputs. After the experimental verification of its perception performance, a recognition model was established based on an extreme learning machine (ELM) algorithm to recognize 10 hand gestures, one of its potential applications, in which 20 subjects participated. It achieved a recognition accuracy of 92.37% and the standard deviation was $\pm$ 1.80% among these individuals, which validated the performance of our designed data glove.