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时间背景信息的肌电特征提取揭示了基于EMG的手势识别中的频率不变.

Rami N Khushaba, Rami Mobarak, Oluwarotimi W Samuel

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

情境感知电肌图 (EMG) 功能提取通过考虑时间趋势来改善手势识别. 这种新的方法实现了高精度,证明了人机界面的采样频率不变性.

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科学领域:

生物医学工程生物医学工程
信号处理信号处理
人与计算机的交互

背景情况:

电肌图 (EMG) 腕带是手势识别的关键.
当前的方法往往忽视时间活动趋势,限制了上下文捕获,导致错误.
现有的空间特征提取方法可以通过结合时间动态来增强.

研究的目的:

开发一个具有上下文意识的电子肌图 (EMG) 功能提取框架.
通过整合短期和长期时间活动趋势来提高手势识别的准确性.
评估拟议方法在不同采样频率和临床群体中的性能.

主要方法:

开发了一个时间上下文框架,封装了基于Phasor的多信号波形长度 (MSWL) 功能.
连锁的短期记忆 (部分相关性) 和长期记忆 (趋势) 信息流.
评估了来自健康受试者的EMG数据集的方法,采样频率不同,以及跨辐射截肢者 (NinaPro协议).

主要成果:

情境感知EMG特征提取证明了手势识别中的采样频率不变性.
在高频和低频腕带中实现了类似的平均准确性 (91%),超过了现有的58种方法.
使用截肢者的数据,在上下文敏感的EMG模式识别中展示了有效性.

结论:

情境感知EMG特征提取可以提高手势识别的准确性和稳定性.
拟议的方法挑战了在EMG应用中对更高采样频率设备的传统偏好.
这种方法对先进的人机界面具有重要的临床意义.