EEG-Driven Physical Load Detection During Human Walking for Robotic and Automation Systems

Understanding neural responses to varying physical loads is essential for developing ergonomic designs. Conventional methods for analyzing peripheral muscle activity, such as electromyography (EMG) and kinematic analysis, provide only limited insight into the cortical dynamics associated with physical tasks. To overcome this limitation, the present study introduces an electroencephalography (EEG)-based approach to investigate brain activity during load-bearing conditions. Participants performed a 100-meter walking task while carrying a 5 kg shoulder load, during which raw EEG signals were recorded. These signals were transformed using Continuous Wavelet Transform (CWT) to generate scalograms, capturing both temporal and frequency-domain characteristics of neural activity. Deep learning (DL) models were then trained, validated, and tested using these representations, and their performance was evaluated through standard metrics. Several DL architectures, including CNN, ResNet18, VGG19, DenseNet, and ResNet50, were employed to extract spatial–temporal features associated with load conditions. Among these, ResNet18 achieved the highest accuracy of 66.83%, outperforming conventional feature-based approaches. Additionally, the occipital cortex showed the highest classification accuracy (69.09%) in distinguishing between no-load and 5 kg load conditions. These findings highlight the potential of DL-based EEG analysis for workload monitoring, fatigue assessment, and brain–computer interface applications.