Low‐Cost Data Glove Based on Deep‐Learning‐Enhanced Flexible Multiwalled Carbon Nanotube Sensors for Real‐Time Gesture Recognition. (2nd October 2022)

• Record Type: Journal Article
• Authors: Li, Yang; Yang, Lina; He, Zhanmei; Liu, Yijian; Wang, Hongfei; Zhang, Wenbin; Teng, Lu; Chen, Da; Song, Ge
• Is Part Of: Advanced intelligent systems. Volume 4:Number 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-02
• Subjects: deep learning models -- gesture recognitions -- human–machine interfaces -- modified wavelet threshold functions -- multiwalled carbon nanotubes -- strain sensors; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202200128
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : With advancements in artificial intelligence, wearable motion recognition systems based on flexible nanomaterial sensors exhibit excellent potential for harmonious human–machine interaction. However, the sensing stability and demand of large‐scale arrays limit the application of flexible nanomaterial sensors. Herein, a data glove system based on simple multiwalled carbon nanotube (MWCNT) sensors and a lightweight deep‐learning algorithm to achieve accurate gesture recognition is proposed. A regional‐crack mechanism is introduced through the microspine structure to enhance the strain sensitivity. Moreover, an efficient signal processing strategy based on an adaptive wavelet threshold function to improve the robustness and anti‐interference of signals obtained from MWCNT sensors, which exhibit strong generalization and can be used in other nanomaterial strain sensor. Based on the depth‐wise separable convolution, a novel hybrid convolutional neural network (CNN) long short‐term memory (LSTM) model for gesture recognition is constructed. The proposed model achieves an average accuracy of 97.5% and recognition accuracy of 30 gestures with an average recognition time of 2.173 ms based on only five sensors. The fabricated data glove is a promising platform for low‐cost and wearable human–machine interaction that can be directly interfaced in applications such as robotic hands, smart cars, and first‐person shooting games. Abstract : Low‐cost data glove based on deep‐learning‐enhanced multiwalled carbon nanotube sensors for real‐time gesture recognition is proposed. The glove is a promising platform for wearable human–machine interaction, achieving an accuracy of 97.5% of 30 gestures with average recognition time of 2.173 ms based on only five sensors, which can be directly interfaced in various applications.