在医疗保健应用中通过使用表面肌电图进行肌肉疲劳评估:一种转移学习方法
Andrea Manni1, Gabriele Rescio1, Andrea Caroppo1
1Institute for Microelectronics and Microsystems, National Research Council of Italy, 73100 Lecce, Italy.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究开发了一种深度学习框架,用于使用电肌图 (EMG) 信号监测老年人肌肉疲劳. 这种新的方法准确地分类了疲劳水平,提高了辅助生活应用的安全性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 肌肉疲劳监测对于老年人的安全和活动支持至关重要.
- 现有的疲劳评估方法在实时应用方面存在局限性.
- 环境辅助生活 (AAL) 需要非侵入性,可靠的监测解决方案.
研究的目的:
- 引入一种新的深度学习框架来对肌肉疲劳水平进行分类.
- 为了利用无线表面电肌图 (sEMG) 数据来检测疲劳.
- 支持开发用于老年护理的AAL应用程序.
主要方法:
- 从老年人和非老年人中收集了一个新的sEMG信号数据集.
- 一维sEMG信号被转换成二维时间频率图像 (scalograms) 使用连续波形变换.
- 预先训练的卷积神经网络 (CNN) 被微调为图像分类,包括二进制和多类疲劳水平检测.
主要成果:
- 深度学习框架在二进制分类中实现了98.6%的准确性 (疲劳与非疲劳).
- 多类分类实现了95.6%的准确性 (没有疲劳,中度疲劳,强度疲劳).
- 拟议的转移学习管道超过了传统的机器学习方法 (最大92%的准确性).
结论:
- 拟议的深度学习框架显示了对肌肉疲劳监测的强大和可泛化的性能.
- 这种方法为AAL场景提供了一个潜在的实时,非侵入性解决方案.
- 这些发现支持将先进的人工智能整合到高级老年护理和安全中.
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