微控制器实现LSTM神经网络,用于动态手势识别
Kevin Di Leo1, Giorgio Biagetti1, Laura Falaschetti1
1DII-Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究表明,使用微控制器上的加速度计识别手势的高精度. 该系统在人类运动识别方面达到90%以上的准确性,即使在资源有限的设备上也是如此.
科学领域:
- 可穿戴技术是可穿戴的技术.
- 机器学习用于人类运动分析
- 嵌入式系统 嵌入式系统
背景情况:
- 便携式设备中的加速度计提供了不引人注目的人类运动数据采集.
- 人类运动识别对于人机交互和健康监测至关重要.
研究的目的:
- 为了评估长期短期记忆 (LSTM) 神经网络在微控制器上进行手势分类的性能.
- 分析嵌入式人类运动识别系统的准确性和资源利用之间的权衡.
主要方法:
- 利用公开可用的数据集,使用手腕加速度计测试10名受试者的20个手势.
- 实现了一个LSTM神经网络模型用于手势分类.
- 在STM32L4系列微控制器上部署和评估模型.
主要成果:
- 实现了近90.25%的准确性,用于手势分类.
- 推断时间为4秒序列的418毫秒.
- 在识别任务中,CPU的平均使用率约为10%.
结论:
- 在资源有限的微控制器上,LSTM神经网络可以有效地执行手势识别.
- 该系统在嵌入式应用程序中展示了高精度和高效资源利用之间的可行平衡.
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