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

Jie Li, Bin Liu*, Yingying Hu, Juan Liu, Xing-Dao He, Jinhui Yuan, Qiang Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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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 basedon D-shaped plastic optical fiber (POF) curvature sensor was proposed and experimentally studied. A highly bend sensitive Dshaped POF curvature sensor was made and integrated into a fivechannel 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.
Original languageEnglish
Pages (from-to)4252-4261
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Issue number4
Early online date22 May 2023
Publication statusPublished - 1 Apr 2024

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