A data analytic framework for physical fatigue management using wearable sensors

Abstract

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.