Predicting mental workload of using exoskeletons for construction work: a deep learning approach

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Abstract
Exoskeletons are gaining attention as a potential solution for addressing back injury in the construction industry. However, using active back-support exoskeletons in construction can trigger unintended consequences which could increase the mental workload of workers. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Predicting mental workload during exoskeleton use could inform strategies to mitigate the triggers. This study investigates two machine-learning frameworks for predicting mental workload using an active back-support exoskeleton for construction work. Laboratory experiments were conducted wherein electroencephalography (EEG) data was collected from participants wearing an active back-support exoskeleton to perform flooring tasks. The EEG data underwent preprocessing, including band filtering, notch filtering, and independent component analysis, to remove artifacts and ensure data quality. A regression-based Long Short-Term Memory (LSTM) network and a hybrid model of convolutional neural network and LSTM were trained to forecast future time steps of the processed EEG data. The performance of the networks was evaluated using root mean square error and r-squared. An average root mean square error of 0.162 and r-squared of 0.939 indicate that the LSTM network has a better predictive power across all the EEG channels. Results of the comparison between the actual and predicted mental workload also show that about 75% of the variance in the actual mental workload is captured in the predicted mental workload. This study enhances understanding of the unintended consequences of using exoskeletons in construction work. The results highlight the effectiveness of various convolutional neural network methods in identifying key EEG data features, offering guidance for algorithm selection in future applications. Additionally, the study identifies the most suitable brain channels for assessing mental workload during exoskeleton use, aiding the development of EEG devices that optimize cost-effectiveness, explanatory power, and minimal channels. This study provides valuable insights for stakeholders to understand the impact of mental workload while using exoskeletons and discovering opportunities for mitigation.

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