A data analytic framework for physical fatigue management using wearable sensors
A data analytic framework for physical fatigue management using wearable sensors
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DOI：
10.1016/j.eswa.2020.113405
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发表时间：
2020-10-01
影响因子：
8.5
通讯作者：
Megahed, Fadel M.
中科院分区：
文献类型：
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作者：
Maman, Zahra Sedighi;Chen, Ying-Ju;Megahed, Fadel M.
由于缺乏对个人表现如何随着疲劳积累而恶化的理解，专家系统在优化和改变人类表现方面的使用在实践中受到限制，疲劳积累可能根据工人和工作场所条件而变化。作为实现以人为中心的人工智能和专家系统方法的第一步，本文为数据分析方法在体力要求高的工作场所管理疲劳奠定了基础。所提出的框架利用可穿戴传感器技术不断收集的人体性能数据，并围绕四个不同的疲劳阶段展开：(a)检测，其中部署机器学习方法来检测疲劳的发生；(b)识别，识别与疲劳发生有关的关键特征；(c)诊断，根据前两阶段产生的知识识别疲劳模式；(d)恢复，采用适当的干预措施使工人恢复，以减轻疲劳对工人的有害影响。此外，该框架还建立了疲劳管理的特征和机器学习算法选择标准。针对两种类型的制造相关任务，给出了该框架的两个具体应用案例。基于提出的框架和两个案例研究中使用的大量测试集，我们已经表明：(i)疲劳检测只需要一个可穿戴传感器，平均精度>= 0.850，随机森林模型包含< 7个特征；（ii）所选择的特征是任务相关的，因此捕获了不同的疲劳模式。因此，这项研究为未来的专家系统提供了重要的基础，该系统试图量化/预测员工绩效的变化，并将其作为规范性休息-休息计划、工作轮换和任务分配模型的输入。为了鼓励未来在这一重要领域的工作，我们提供了我们的数据和代码的链接作为补充材料。(C) 2020 Elsevier Ltd.版权所有。
The use of expert systems in optimizing and transforming human performance has been limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue accumulation, which can vary based on both the worker and the workplace conditions. As a first step toward realizing the human-centered approach to artificial intelligence and expert systems, this paper lays the foundation for a data analytic approach to managing fatigue in physically-demanding workplaces. The proposed framework capitalizes on continuously collected human performance data from wearable sensor technologies, and is centered around four distinct phases of fatigue: (a) detection, where machine learning methodologies are deployed to detect the occurrence of fatigue; (b) identification, where key features relating to the fatigue occurrence is to be identified; (c) diagnosis, where the fatigue mode is identified based on the knowledge generated in the previous two phases; and (d) recovery, where a suitable intervention is applied to return the worker to mitigate the detrimental effects of fatigue on the worker. Moreover, the framework establishes criteria for feature and machine learning algorithm selection for fatigue management. Two specific application cases of the framework, for two types of manufacturing-related tasks, are presented. Based on the proposed framework and a large number of test sets used in the two case studies, we have shown that: (i) only one wearable sensor is needed for fatigue detection with an average accuracy of >= 0.850 and a random forest model comprised of < 7 features; and (ii) the selected features are task-dependent, and thus capturing different modes of fatigue. Therefore, this research presents an important foundation for future expert systems that attempt to quantify/predict changes in workers' performance as an input to prescriptive rest-break scheduling, job-rotation, and task assignment models. To encourage future work in this important area, we provide links to our data and code as Supplementary materials. (C) 2020 Elsevier Ltd. All rights reserved.