使用可穿戴设备和人工智能的疲劳监测:趋势,挑战和未来的机会
Kourosh Kakhi1, Senthil Kumar Jagatheesaperumal2, Abbas Khosravi1
1Institute for Intelligent Systems Research and Innovation (IISRI), Geelong, 3217, VIC, Australia.
Computers in biology and medicine
|June 28, 2025
概括
可穿戴技术和人工智能 (AI) 可以通过分析生理信号来准确检测工人疲劳. 这种整合通过预测耗尽和防止相关危险,提高了高风险行业的安全性.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 人工智能的人工智能
背景情况:
- 疲劳监测对于运输和医疗保健等苛刻职业的安全至关重要.
- 可穿戴设备可以实时进行生理信号分析以检测疲劳.
- 及时的疲劳洞察力有助于预防工作场所的危险.
研究的目的:
- 审查可穿戴技术和人工智能用于疲劳检测的整合.
- 系统地分析与疲劳相关的生理数据的信号处理方法.
- 评估人工智能模型在识别疲劳模式方面的有效性.
主要方法:
- 按照PRISMA方法进行系统审查.
- 对生理数据 (ECG,EMG,EEG) 的信号处理技术的分析.
- 机器学习/深度学习模型用于疲劳模式识别的应用.
主要成果:
- 可穿戴设备与人工智能模型相结合,通过多模式信号分析准确地识别疲劳.
- 信息融合技术显著提高了疲劳评估的精度和可靠性.
- 人工智能有效地检测模式并预测疲劳的开始.
结论:
- 可穿戴技术和人工智能为实时疲劳监测提供了强大的解决方案.
- 多模式信号分析和信息融合提高了检测准确度.
- 这些进展为提高职业安全和减少疲劳相关风险提供了有价值的工具.
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