论文标题
使用ECG和Actraphy传感器进行疲劳评估
Fatigue Assessment using ECG and Actigraphy Sensors
论文作者
论文摘要
疲劳是失去工作效率和与健康相关的生活质量的关键因素之一，大多数疲劳评估方法都是基于自我报告的，这可能会遭受许多因素，例如回忆偏见。为了解决这个问题，我们使用可穿戴感应和机器学习技术开发了一个自动化系统，以进行客观的疲劳评估。在自由生活环境中，从受试者中收集了心电图/动作法数据。在引入可解释的解决方案和深度学习解决方案之前，应用了预处理和特征工程方法。具体来说，对于可解释的解决方案，我们提出了一种功能选择方法，该方法可以选择较少的相关和高信息的功能，以更好地了解系统的决策过程。对于深度学习解决方案，我们使用了最先进的自我注意力学模型，基于该模型，我们进一步提出了一种一致性自我注意（CSA）机制来进行疲劳评估。进行了广泛的实验，并实现了非常有希望的结果。
Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system's decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.
