Plastic-Optical-Fiber-Enabled Smart Glove for Machine-Learning-Based Gesture Recognition
Jie Li, Bin Liu, Yingying Hu, Juan Liu, Xingdao He, Jinhui Yuan, Qiang Wu
Abstract
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.