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使用机器学习区分手势与前臂肌肉活动

Ryan Cho¹, Sunil Puli², Jaejin Hwang²

¹Illinois Mathematics and Science Academy, USA.

International journal of occupational safety and ergonomics : JOSE

|August 14, 2024

概括

这项研究使用前臂肌电图信号来识别八种手势. 随机森林 (RF) 在更大的数据窗口中实现了97%的准确性,而神经网络 (NN) 在增加时间分辨率的准确性方面表现出色.

科学领域:

生物医学工程生物医学工程
机器学习机器学习
人与计算机的交互

背景情况:

电肌图 (EMG) 信号提供了一种非侵入性的方法来捕捉神经肌肉活动.
精确的手势识别对于先进的假肢,机器人和虚拟现实接口至关重要.
评估基于EMG的手势分类的机器学习算法对于系统开发至关重要.

研究的目的:

为了比较随机森林 (RF) 和神经网络 (NN) 算法的性能,使用前臂电肌图 (EMG) 数据对八种不同的手势进行分类.
调查不同时间分辨率 (窗口大小) 对RF和NN算法的精度的影响.
确定最佳算法和窗口大小,用于需要高精度或快速响应时间的应用程序.

主要方法:

从10名参与者中收集了前臂EMG数据,他们执行了8种不同的手势.
两种机器学习算法,即随机森林 (RF) 和神经网络 (NN),用于分类.
系统分析了从200ms到1000ms的数据窗口大小对分类准确性的影响.

主要成果:

随机森林 (RF) 的准确性从85%增加到97%,因为窗口大小从200 ms增加到1000 ms.
在较小的窗口大小 (200毫秒) 中,RF 实现了 85% 的准确性,在 80% 的性能上超过了 NN.
随着窗口大小的增加,NN性能得到了改善,这表明它适合于优先考虑准确性而不是速度的应用.

结论:

两种RF和NN算法都显示出基于EMG的手势识别的潜力,性能取决于时间分辨率.
对于要求快速响应时间的应用,RF是有利的,而NN更适合需要更高分类准确度的场景.
未来的研究应该专注于更大的样本大小,多样化的手势集,先进的功能提取和新的算法来提高系统性能.

关键词:

电动肌图学电动肌图学扩展现实 (AR) 是一种扩展现实.机器学习是机器学习.信号分析信号分析

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