Self‐Powered Gesture Recognition Wristband Enabled by Machine Learning for Full Keyboard and Multicommand Input. Issue 21 (27th April 2022)

• Record Type: Journal Article
• Authors: Tan, Puchuan; Han, Xi; Zou, Yang; Qu, Xuecheng; Xue, Jiangtao; Li, Tong; Wang, Yiqian; Luo, Ruizeng; Cui, Xi; Xi, Yuan; Wu, Le; Xue, Bo; Luo, Dan; Fan, Yubo; Chen, Xun; Li, Zhou; Wang, Zhong Lin
• Is Part Of: Advanced materials. Volume 34:Issue 21(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-27
• Subjects: gesture recognition -- human–computer interaction -- machine learning -- triboelectric nanogenerator -- virtual reality; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202200793
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Virtual reality is a brand‐new technology that can be applied extensively. To realize virtual reality, certain types of human–computer interaction equipment are necessary. Existing virtual reality technologies often rely on cameras, data gloves, game pads, and other equipment. These equipment are either bulky, inconvenient to carry and use, or expensive to popularize. Therefore, the development of a convenient and low‐cost high‐precision human–computer interaction device can contribute positively to the development of virtual reality technology. In this study, a gesture recognition wristband that can realize a full keyboard and multicommand input is developed. The wristband is convenient to wear, low in cost, and does not affect other daily operations of the hand. This wristband is based on physiological anatomy as well as aided by active sensor and machine learning technology; it can achieve a maximum accuracy of 92.6% in recognizing 26 letters. This wristband offers broad application prospects in the fields of gesture command recognition, assistive devices for the disabled, and wearable electronics. Abstract : In this work, a gesture recognition wristband for virtual reality application with excellent prediction accuracy and good wearing experience is manufactured. This easy‐to‐wear wristband can translate and broadcast sign language words in real time with a prediction accuracy over 92.6%. And the gesture recognition wristband can also be designed as a special command input device.