Hajifar Sahand, Sun Hongyue, Megahed Fadel M, Jones-Farmer L Allison, Rashedi Ehsan, Cavuoto Lora A
Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA.
Farmer School of Business, Miami University, Oxford, OH 45056, USA.
Appl Ergon. 2021 Jan;90:103262. doi: 10.1016/j.apergo.2020.103262. Epub 2020 Sep 11.
Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker's condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.
传感和网络技术的进步增加了为监测工人状况而收集的数据量。在本研究中，我们考虑使用时间序列方法，通过主观用力感觉评分（RPE）以及在模拟的实验室手动物料搬运任务（实验室研究1）和疲劳深蹲与间歇性步行周期（实验室研究2）期间从可穿戴传感器捕获的步态数据来预测身体疲劳。为了确定时间序列模型是否能够准确预测个体反应以及提前预测多少个时间段，我们比较了五个模型：朴素方法、自回归（AR）、自回归积分移动平均（ARIMA）、向量自回归（VAR）和向量误差校正模型（VECM）。对于提前三个或更多时间段的预测，结合历史RPE和可穿戴传感器数据的VECM模型在实验室研究1和实验室研究2中分别以中位数平均绝对误差（MAE）<1.24和中位数MAE<1.22优于其他模型。这些结果表明，可穿戴传感器数据可以支持对工人状况的预测，并且所获得的预测与当前使用多个传感器进行当前时间预测的最先进模型一样好。
