Comparative Performance of IMU and sEMG in Locomotion Mode Prediction Across Transitional and Steady-State...

Accurate and robust locomotion mode prediction is crucial for seamless interaction between humans and assistive devices. While multimodal sensing offers a promising avenue for enhanced accuracy, existing approaches often struggle to demonstrate clear advantages over unimodal methods, largely due to a lack of understanding regarding each modality's unique characteristics and task-specific strengths. To address this, we present a systematic comparative analysis of Inertial Measurement Unit (IMU) and surface Electromyography (sEMG) modalities for human locomotion mode prediction. Utilizing a public dataset (nine subjects, 17 gait activities), our experiments rigorously evaluated performance across cyclic and non-cyclic locomotion tasks, considering both steady-state and transition-state. We investigated the impact of deep learning architectures (CNN, LSTM, TCN) and sliding-window lengths (short vs. long) on prediction accuracy and stability. Statistical analyses reveal significant performance differences dependent on modality, window length, and gait type. Notably, our findings demonstrate that optimal classification accuracy is achieved by leveraging IMU data with short windows for non-cyclic locomotion modes prediction and sEMG data with long windows for cyclic locomotion modes prediction, with Temporal Convolutional Networks (TCNs) consistently yielding superior overall results. These findings offer concrete guidelines for effectively fusing multimodal data, leveraging modality-specific strengths to enable adaptive, interpretable, and high-performance locomotion mode prediction.