Plastic Optical Fiber Enabled Smart Glove for Machine Learning-Based Gesture Recognition

• Published in: IEEE transactions on industrial electronics (1982) 2024-04, Vol.71 (4), p.1-10
• Main Authors: Li, Jie, Liu, Bin, Hu, Yingying, Liu, Juan, He, Xing-Dao, Yuan, Jinhui, Wu, Qiang
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bending; Curvature; D-shaped POF curvature sensor; FNN; Gaussian kernel function; Gesture recognition; human-computer interaction (HCI); Kernel functions; Machine learning; Optical fiber amplifiers; Optical fiber sensors; Optical fibers; Sensors; Support vector machines; SVM
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2023.3277119

Gesture recognition has always been an important research direction in the field of human-computer interaction (HCI). In this paper, a wearable gesture recognition system based on D-shaped plastic optical fiber (POF) curvature sensor was proposed and experimentally studied. A highly bend sensitive D-shaped POF curvature sensor was made and integrated into a five-channel signal acquisition system on a PCB board (8×4.5 cm), which was embedded into an elastic glove to collect fingers' movement data. Thirteen gestures and eleven grasping actions were defined, and the gesture data, the grasping action data and the gesture data mixed with grasping action data were normalized, calibrated and imported into a support vector machine (SVM) classifier based on Gaussian kernel function and feedforward neural networks (FNN) respectively. The recognition accuracy based on SVM of 13 gestures and 11 grasping actions reached 99.8% and 97.7% respectively. The recognition accuracy of 13 kinds of gesture data mixed with 11 kinds of grasping action data based on Gaussian kernel function in SVM classification model and FNN were 98.9% and 99.4% respectively.