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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. [ABSTRACT FROM AUTHOR]

Effective training programs are essential for safely integrating exoskeletons (EXOs) in industrial workplaces. Since the effects of wearable systems depend highly upon their proper use, lack of training of end-users may cause adverse effects on users. We reviewed articles that incorporated training and familiarization protocols to train novices on proper operation/use of EXOs. Findings showed variation in training methods that were implemented to train study participants in EXO evaluation studies. Studies also indicate that multiple (up to four) sessions may be needed for novice EXO wearers to match movement patterns of experts, and training can offer benefits in enhancing motor learning in novices. Biomechanical assessments and ergonomic evaluations can be helpful in developing EXO-specific training protocols by determining training parameters (duration/number of sessions and task difficulty). Future directions include development of personalized training approaches by assessing user behavior/performance through integration of emerging sensing technologies. Application of simulators and use of data-driven approaches for customizing training protocols to individuals, tasks, and EXO design are provided along with a comprehensive training framework. Discussed elements in this article can be helpful to exoskeleton researchers in familiarizing novice users to EXOs prior to evaluation, and to practitioners in developing protocols for training workforce. [ABSTRACT FROM AUTHOR]