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Gesture Recognition Based on a Convolutional Neural Network–Bidirectional Long Short-Term Memory Network for a Wearable Wrist Sensor with Multi-Walled Carbon Nanotube/Cotton Fabric Material

Authors :
Yang Song
Mengru Liu
Feilu Wang
Jinggen Zhu
Anyang Hu
Niuping Sun
Source :
Micromachines, Vol 15, Iss 2, p 185 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Flexible pressure sensors play a crucial role in detecting human motion and facilitating human–computer interaction. In this paper, a type of flexible pressure sensor unit with high sensitivity (2.242 kPa−1), fast response time (80 ms), and remarkable stability (1000 cycles) is proposed and fabricated by the multi-walled carbon nanotube (MWCNT)/cotton fabric (CF) material based on a dip-coating method. Six flexible pressure sensor units are integrated into a flexible wristband and made into a wearable and portable wrist sensor with favorable stability. Then, seven wrist gestures (Gesture Group #1), five letter gestures (Gesture Group #2), and eight sign language gestures (Gesture Group #3) are performed by wearing the wrist sensor, and the corresponding time sequence signals of the three gesture groups (#1, #2, and #3) from the wrist sensor are collected, respectively. To efficiently recognize different gestures from the three groups detected by the wrist sensor, a fusion network model combined with a convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) neural network, named CNN-BiLSTM, which has strong robustness and generalization ability, is constructed. The three types of Gesture Groups were recognized based on the CNN-BiLSTM model with accuracies of 99.40%, 95.00%, and 98.44%. Twenty gestures (merged by Group #1, #2, and #3) were recognized with an accuracy of 96.88% to validate the applicability of the wrist sensor based on this model for gesture recognition. The experimental results denote that the CNN-BiLSTM model has very efficient performance in recognizing different gestures collected from the flexible wrist sensor.

Details

Language :
English
ISSN :
15020185 and 2072666X
Volume :
15
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Micromachines
Publication Type :
Academic Journal
Accession number :
edsdoj.112d43f99f48456ab59641720ecf49e4
Document Type :
article
Full Text :
https://doi.org/10.3390/mi15020185