Multi-task Learning for Gait Phase and Gait Cycle Percentage Prediction with Wearable Sensors in Frail Older Adults

Deep learning has been widely used in wearable sensors to improve accuracy in gait analysis. However, these deep learning models typically focus on single tasks, either in gait parameter estimation or gait phase detection. This study presents a novel multi-task learning framework for regression (i.e., gait cycle percentage prediction) and classification (i.e., gait phase prediction) tasks in pathological gait analysis using wearable sensors. Our framework employs a Multi-gate Mixture-of-Experts (MMoE) architecture to achieve soft parameter-sharing, integrating expert networks, cross-expert attention mechanisms, and dynamic routing to balance shared and task-specific representations. To reduce computational burden in wearable applications, we compare lightweight model configurations that optimize expert count and feature dimensionality. Model performance has been validated on a public dataset consisting of 158 frail older adults, demonstrating that our framework significantly outperforms single-task learning and hard parameter-sharing baselines, achieving an accuracy of 97.56% and a Mean Absolute Error (MAE) of 0.0397. Notably, the most compact lightweight configuration reduces the parameter count by nearly 98% (from 2.118 million to 0.0469 million), achieving an accuracy of 96.47% and a MAE of 0.0549. Attention mechanisms significantly enhance performance across all configurations, with improvements ranging from 17.9% to 30.4%. These findings validate the potential of lightweight multi-task approaches for real-time gait assessment, offering promising applications for clinical evaluation and rehabilitation monitoring in geriatric populations.