基于人类前臂 flexors 的高密度肌电的手势识别
Xiaoling Chen1,2, Huaigang Yang1, Dong Zhang1
1Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究引入了一种新的特征选择方法 (MPP),用于基于电肌图的手势识别. 它通过为个人用户选择最佳功能来提高准确性和效率,降低计算成本.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 基于电肌图 (EMG) 的手势识别对于解码精细的手部运动至关重要.
- 为了提高准确度,模型复杂度不断增加,需要大量的数据,从而增加用户负担和计算成本.
- 用户之间表面EMG (sEMG) 信号的变化挑战了传统的单特征机器学习方法.
研究的目的:
- 开发一种使用sEMG信号进行特定用户手势识别的计算效率高,准确的方法.
- 解决复杂模型的局限性和目前手势识别系统中单一特征依赖的局限性.
- 在基于EMG的系统中提高跨用户模式识别性能.
主要方法:
- 提出了一种结合相互信息,主要成分分析和皮尔森相关系数 (MPP) 的新型特征选择方法.
- 该MPP方法旨在确定最优的特征子集,以个别用户量身定制.
- 选择的功能与支持矢量机 (SVM) 分类器集成,用于手势识别.
主要成果:
- 拟议的MPP功能选择方法在过特定用户的最佳功能方面表现出有效性.
- 与使用单个特征相比,手势识别准确度提高了约5%,当使用最佳选择的特征子集时使用单个特征.
- 结合MPP和SVM的方法实现了对用户特定的手势运动的准确和有效的识别.
结论:
- 基于EMG的手势识别中,MPP特征选择方法为降低计算成本提供了有效的解决方案.
- 这种方法通过适应用户特定的sEMG信号变化来提高精细手动解码的精度.
- 该研究为特定用户的手势识别提供了强大的框架,为改善人机交互铺平了道路.
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