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Anna Filipowska¹, Wojciech Filipowski², Paweł Raif¹

¹Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.

Sensors (Basel, Switzerland)

|September 28, 2024

概括

研究人员开发了一种低成本的智能手套,使用,力和IMU传感器来识别游戏动态手势. 一个卷积神经网络实现了90%的准确性,提供了一个具有成本效益的手势识别解决方案.

科学领域:

人与计算机的交互 (HCI)
机器人和可穿戴技术
机器学习用于手势识别

背景情况:

在HCI中,手势识别至关重要,智能手套是关键设备.
现有的数据手套研究主要集中在静态手势上,忽视了动态手势识别.
动态手势识别对于游戏和虚拟环境等交互应用程序至关重要.

研究的目的:

开发一个低成本的智能手套原型,用于捕捉和分类动态的手势.
实现基于神经网络的分类器,用于准确的动态手势识别.
评估原型对于游戏控制应用的有效性.

主要方法:

一个原型数据手套被设计成五个曲传感器,五个力传感器和一个惯性测量单元 (IMU).
一个卷积神经网络 (CNN) 分类器与三个2D卷积层和RELU激活被开发用于动态手势分类.
使用准确性,精度和回忆等指标来评估性能,并以混矩阵分析为支持.

主要成果:

开发的智能手套原型成功捕获和分类动态手势.
基于CNN的分类器在手势识别方面取得了90%的高准确度.
该系统展示了高分类准确性,精度和回忆,表明有效的性能.

结论:

低成本的智能手套为动态手势识别提供了具有成本效益和准确的解决方案.
该原型显示了在游戏和虚拟/增强现实 (VR/AR) 环境中应用的巨大潜力.
进一步的研究可以扩大手势词汇和参与者池,以获得更广泛的适用性.

关键词:

动态的姿态动态的姿态这是手势识别,是手势识别.智能手套是一款智能手套.可穿戴设备可穿戴设备.

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