A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors
A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors
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DOI：
10.1016/j.apergo.2020.103262
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发表时间：
2021-01-01
影响因子：
3.2
通讯作者：
Cavuoto, Lora A.
中科院分区：
文献类型：
--
作者：
Hajifar, Sahand;Sun, Hongyue;Cavuoto, Lora A.
传感和网络技术的进步增加了为监控员工条件而收集的数据量。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，这些数据是在模拟的实验室手动材料处理任务(实验室研究1)和疲劳蹲下间歇行走周期(实验室研究2)中捕获的。为了确定时间序列模型是否能够准确地预测个体的反应以及未来几个时间段，比较了五种模型：朴素方法、自回归(AR)、自回归综合移动平均(ARIMA)、向量自回归(VAR)和向量误差修正模型(VECM)。对于未来三个或更长时间段的预测，包含历史RPE和可穿戴传感器数据的VECM模型的表现优于其他模型，在实验室研究1和实验室研究2的所有参与者中，中位数平均绝对误差(MAE)和中位数MAE<；分别为1.24和1.22。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，获得的预测结果与当前使用多个传感器预测当前时间的最先进模型一样好。
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: naive 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.