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

IF 1.6 4区工程技术 Q3 INSTRUMENTS & INSTRUMENTATION Sensor Review Pub Date : 2024-03-25 DOI:10.1108/sr-07-2023-0301

Boyang Hu, Ling Weng, Kaile Liu, Yang Liu, Zhuolin Li, Yuxin Chen

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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.

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磁致伸缩传感手套的结构设计及其在手势识别中的应用

目的手势识别在人机交互、医疗康复、虚拟现实和增强现实等许多领域发挥着重要作用。使用可穿戴设备进行手势识别是一种常见而有效的识别方法。本研究旨在结合反向磁致伸缩效应和隧道磁阻效应，提出一种应用于手势识别领域的新型可穿戴传感手套。设计/方法/途径基于铁-镍合金、隧道磁阻元件、Agilus30 底座和方形永久磁铁，提出了一种具有手势识别功能的磁致伸缩传感手套。传感手套由五个传感单元组成，用于测量每个手指关节的弯曲角度。传感单元的最佳结构是通过实验和模拟确定的。建立了传感单元的输出电压模型，并通过实验平台测试了传感单元的输出特性。研究结果传感单元可以检测到手指关节弯曲角度从 0 度到 105 度的变化，实验值和理论值之间的最大误差为 4.69%。后向传播神经网络对 15 种手势的平均识别准确率为 97.53%。研究局限/意义目前，传感手套只能识别静态手势，要识别动态手势仍需进一步研究。社会意义本研究在人机交互领域具有广阔的应用前景。独创性/价值传感手套可以采集不同手势下的电压信号，实现对不同手势的识别，重复性好，在人机交互领域具有广阔的应用前景。

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来源期刊

Sensor Review 工程技术-仪器仪表

CiteScore

3.40

自引率

6.20%

发文量

审稿时长

3.7 months

期刊介绍： Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments. Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles. All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable. Sensor Review’s coverage includes, but is not restricted to: Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors Temperature sensors, infrared sensors, humidity sensors Optical, electro-optical and fibre-optic sensors and systems, photonic sensors Biosensors, wearable and implantable sensors and systems, immunosensors Gas and chemical sensors and systems, polymer sensors Acoustic and ultrasonic sensors Haptic sensors and devices Smart and intelligent sensors and systems Nanosensors, NEMS, MEMS, and BioMEMS Quantum sensors Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.