Finger Motion Gesture Recognition Based on Double-Clad Fiber Bragg Grating Sensor Array
ZHANG Sen1, 2, LIU Yi-xin1, ZHANG Zeng-ya1, ZHOU Jian-wen1, LU Jun-yu1, CAO Shan-shan3, YU Ke-han1, 4, WEI Wei1, 4, ZHENG Jia-jin1, 4*
1. College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2. China Telecom Group Shandong Branch, Jinan 250101, China
3. Jiangsu Zhongtian Technology Co., Ltd., Nantong 226009, China
4. Jiangsu Province Engineering Research Center for Fabrication and Application of Special Optical Fiber Materials and Devices, Nanjing 210023, China
Abstract：Finger motion gesture recognition has broad application prospects in remote medical care, intelligent wearable devices, and human-computer interaction. However, many challenges still exist in achieving natural and fluent finger motion gesture recognition. In this paper, we proposed a finger motion gesture recognition system based on a double-clad fiber Bragg grating (FBG) sensor array. Six double-clad FBG sensors with different central wavelengths were deployed at various positions on the subjects' forearms. The system can recognize the small deformations of different muscle tissues caused by finger motion in real-time, and it will not affect the natural and fluent motion of the fingers. The system achieved high accuracy recognition of nearly 100% of unknown simple gestures and about 85% of complex gestures based on the double-clad FBG center wavelength shift of six known static gestures. The same experiment was conducted using single-mode FBG. The results showed that the double-clad FBG could effectively recognize simple gestures of single-finger motion and complex gestures of multiple-finger motion. In contrast, the single-mode FBG can only recognize gestures of single-finger motion. The double-clad FBG sensor array finger motion gesture recognition system proposed in this paper not only ensures high-precision deformation measurement to recognize different gestures but also has good stability and anti-interference ability. These results indicate that the research work in this paper has great potential in gesture recognition.
