Foot gesture recognition with flexible high-density device based on convolutional neural network

Lin，Chengyu1; Tang，Yuxuan1; Zhou，Yong1; Zhang，Kuangen1,2; Fan，Zixuan3; Yang，Yang1; Leng，Yuquan1; Fu，Chenglong1

10.1109/ICARM52023.2021.9536141

2021-07-03

6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

978-1-6654-4596-2

2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021

306-311

JUL 03-05, 2021

null,Chongqing,PEOPLES R CHINA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.

Wireless communication Wearable computers Training data Gesture recognition Muscles Electromyography Skin

第一 ; 通讯

英语

CPCI-S ; EI

National Key R&D Program of China["2018YFB1305400","2018YFC2001601"]

Automation & Control Systems ; Engineering ; Robotics

Automation & Control Systems ; Engineering, Electrical & Electronic ; Robotics

WOS:000728141500051

20214010982464

Convolution ; Convolutional neural networks ; Gesture recognition ; Muscle ; Wearable technology

Biological Materials and Tissue Engineering:461.2 ; Prosthetics:462.4 ; Information Theory and Signal Processing:716.1

2-s2.0-85116191862

Scopus

1.Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems,Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities,Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.The Department of Mechanical Engineering,The University of British Columbia,Vancouver,V6T1Z4,Canada
3.The School of Modern Post,Beijing University of Posts and Telecommunications,Beijing,100876,China

Lin，Chengyu,Tang，Yuxuan,Zhou，Yong,et al. Foot gesture recognition with flexible high-density device based on convolutional neural network[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:306-311.