Dual-Stage Attention-Based Model for Rehabilitation Activity Recognition Using Data From Wearable Sensors

This study addresses the critical need for effective rehabilitation monitoring for individuals with gait issues through the integration of interpretable artificial intelligence (AI) models. The study meticulously details the methodology, emphasizing the systematic data collection process, participant categorization based on gait severity, and the deployment of a novel physical activity monitoring device. Through the lens of the Berg balance test, the analysis scrutinizes functional mobility and balance across seven rehabilitation activities. Data augmentation techniques and data preprocessing methods are employed, and the proposed hybrid model combines both time and frequency-domain data using a multibranch CNN-BiLSTM architecture embedded with a dual-stage attention mechanism. The study compares our approach with machine learning and deep learning models, showcasing the superior performance of the proposed interpretable architecture. Interpretability is highlighted through attention mechanisms, revealing the model’s prioritization of fast Fourier transform-transformed data and its ability to identify influential branches. Our model provides transparent insights to healthcare professionals for personalized rehabilitation strategies. These results show high potential in developing real-time applications, including the integration of additional sensor modalities, interdisciplinary collaborations, and the expansion of demographic inclusivity for more robust and applicable rehabilitation monitoring solutions.