Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, USA.
Sensors (Basel). 2024 Sep 14;24(18):5971. doi: 10.3390/s24185971.
Forecasting can be utilized to predict future trends in physiological demands, which can be beneficial for developing effective interventions. This study implemented forecasting models to predict fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were utilized from nine recruited participants who performed 45° trunk flexion tasks intermittently with and without assistance until they reached medium-high exertion in the low-back region. Two forecasting algorithms, Autoregressive Integrated Moving Average (ARIMA) and Facebook Prophet, were implemented using perceived fatigue levels alone, and with external features of low-back muscle activity. Findings showed that univariate models without external features performed better with the Prophet model having the lowest mean (SD) of root mean squared error (RMSE) across participants of 0.62 (0.24) and 0.67 (0.29) with and without EXO-assisted tasks, respectively. Temporal effects of BSIE on delaying fatigue progression were then evaluated by forecasting back fatigue up to 20 trials. The slope of fatigue progression for 20 trials without assistance was ~48-52% higher vs. with assistance. Median benefits of 54% and 43% were observed for ARIMA (with external features) and Prophet algorithms, respectively. This study demonstrates some potential applications for forecasting models for workforce health monitoring, intervention assessment, and injury prevention.
预测可以用于预测生理需求的未来趋势，这对于制定有效的干预措施是有益的。本研究实施了预测模型，以预测在执行外骨骼（EXO）辅助任务时疲劳水平的进展。具体来说，从九名招募的参与者那里收集了感知和肌肉活动数据，他们间歇性地进行 45°躯干屈曲任务，同时有和没有辅助，直到他们的下背部达到中高强度的疲劳。使用仅感知疲劳水平以及下背部肌肉活动的外部特征，实施了两种预测算法，自回归综合移动平均（ARIMA）和 Facebook Prophet。结果表明，没有外部特征的单变量模型表现更好， Prophet 模型在没有和有 EXO 辅助任务的情况下，参与者的均方根误差（RMSE）的最低均值（SD）分别为 0.62（0.24）和 0.67（0.29）。然后通过预测 20 次试验后的背部疲劳来评估 BSIE 对疲劳进展的延迟的时间效应。没有辅助的 20 次试验的疲劳进展斜率比有辅助的高约 48-52%。ARIMA（带外部特征）和 Prophet 算法分别观察到 54%和 43%的中位数收益。本研究证明了预测模型在劳动力健康监测、干预评估和伤害预防方面的一些潜在应用。
