Self-Attention Enhanced Spatio-Temporal Convolution for ADL Recognition in Elderly Care

The increasing aging population necessitates intelli-gent monitoring systems that can accurately detect and classify daily activities, enabling proactive healthcare interventions and promoting independent living for the elderly. To this end, this study presents ADL recognition system for elderly care, leveraging advanced 3D CNNs with skip connections and self-attention mechanism. Our proposed model processes RGB video data from the ETRI-Activity3D dataset. The architecture incorporates skip connections to mitigate the vanishing gradient problem and a self-attention mechanism to capture complex spatiotemporal features. Experimental results demonstrate the superiority of our approach, achieving 99.0% accuracy on the training set and 96.1% on the validation set. Notably, our model outperforms the baseline ETRI(FSA-CNN) implementation by 6%. The study provides both quantitative and qualitative evaluations, including accuracy metrics, loss curves, and visual representations of classification outcomes. These thorough results highlight the robustness and effectiveness of our proposed system in real-world scenarios. This research contributes to the evolving landscape of technology-assisted elderly care, offering a foundation for future developments in anomaly detection, risk management, and personalized alert systems. The proposed ADL recognition system has the potential to greatly improve the quality of life for elderly individuals and streamline healthcare resource allocation.