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用于监控工人状况的数据量。在这项研究中，我们考虑使用时间序列方法来预测身体疲劳，使用感知劳累 (RPE) 的主观评级和来自可穿戴传感器的步态数据在模拟实验室手动材料处理任务（实验室研究 1）和疲劳间歇性步行循环下蹲（实验室研究 2）。为了确定时间序列模型是否可以准确预测个体响应以及未来多少个时间段，我们比较了五种模型：朴素方法、自回归 (AR)、自回归积分移动平均 (ARIMA)、向量自回归 (VAR) 和向量误差修正模型 (VECM)。对于未来三个或更多时间段的预测，$<1.24$ 和中值 MAE $<1.22$分别针对实验室研究 1 和实验室研究 2 的所有参与者。这些结果表明，可穿戴传感器数据可以支持预测工人的状况，并且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。