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Structural design of magnetostrictive sensing glove and its application for gesture recognition

Boyang Hu (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)
Ling Weng (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)
Kaile Liu (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)
Yang Liu (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)
Zhuolin Li (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)
Yuxin Chen (School of Electrical Engineering, Hebei University of Technology, Tianjin, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 25 March 2024

Issue publication date: 9 April 2024

32

Abstract

Purpose

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

Design/methodology/approach

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

Findings

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

Research limitations/implications

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

Practical implications

A new approach to gesture recognition using wearable devices.

Social implications

This study has a broad application prospect in the field of human–computer interaction.

Originality/value

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Keywords

Acknowledgements

This work was supported by the National Natural Science Foundation of China 52377007, 52077052, the Natural Science Foundation of Hebei Province E2022202067, the Central Leading Local Science and Technology Development Fund Project 226Z1704G, and the Hebei Higher Education Scientific Research Project JZX2023011.

Citation

Hu, B., Weng, L., Liu, K., Liu, Y., Li, Z. and Chen, Y. (2024), "Structural design of magnetostrictive sensing glove and its application for gesture recognition", Sensor Review, Vol. 44 No. 2, pp. 113-121. https://doi.org/10.1108/SR-07-2023-0301

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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