计算机科学
手势识别
卷积神经网络
可穿戴计算机
电容感应
人工智能
手势
稳健性(进化)
特征提取
计算机视觉
计算机硬件
嵌入式系统
生物化学
基因
操作系统
化学
作者
Tiantong Wang,Yunbiao Zhao,Qining Wang
出处
期刊:Soft robotics
[Mary Ann Liebert]
日期:2023-06-01
卷期号:10 (3): 443-453
被引量:1
标识
DOI:10.1089/soro.2021.0209
摘要
Hand gesture recognition, one of the most popular research topics in human–machine interaction, is extensively used in visual and augmented reality, sign language translation, prosthesis control, and so on. To improve the flexibility and interactivity of wearable gesture sensing interfaces, flexible electronic systems for gesture recognition have been widely studied. However, these systems are limited in terms of wearability, stability, scalability, and robustness. Herein, we report a flexible wearable hand gesture recognition system that is based on an iontronic capacitive pressure sensing array and deep convolutional neural networks. The entire capacitive array is integrated into a flexible silicone wristband and can be comfortably and conveniently wrapped around the wrist. The pressure sensing array, which is composed of an iontronic film sandwiched between two flexible screen-printed electrode arrays, exhibits a high sensitivity (775.8 kPa−1), fast response time (65 ms), and high durability (over 6000 cycles). Image processing techniques and deep convolutional neural networks are applied for sensor signal feature extraction and hand gesture recognition. Several contexts such as intertrial test (average accuracy of 99.9%), intersession rewearing (average accuracy of 93.2%), electrode shift (average accuracy of 83.2%), and different arm positions during measurement (average accuracy of 93.1%) are evaluated.
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