Deep Learning-Based ADL Assessment and Personalized Care Planning Optimization in Adult Day Health Centers

This paper presents an in-depth learning process for Activities of Daily Living (ADL) and self-care planning in Adult Day Health Centers. The framework integrates multi-modal sensor data fusion with deep learning architectures to provide continuous monitoring and automated evaluation of older people's legal status. The system uses a hierarchical system combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, enhanced by monitoring systems for physical-spatial learning. Many data streams from wearable devices, environmental sensors, and medical monitoring devices are becoming pre-processed and de-processed. The framework incorporates a dynamic care plan adaptation strategy utilizing reinforcement learning techniques for intervention optimization. Experimental validation conducted across three Adult Day Health Centers with 150 participants over six months demonstrated superior performance compared to traditional assessment methods. The system achieved 92.8% accuracy in ADL recognition tasks, with a 35% reduction in assessment time and a 62% decrease in false alarm rates. Clinical validation through 25 detailed case studies revealed early detection of health deterioration, averaging 3.2 days ahead of conventional methods. The proposed framework significantly enhances the efficiency and accuracy of elderly care delivery while reducing healthcare provider workload by 40%. This research contributes to advancing intelligent healthcare by creating a solution for ADL measurement and self-correction.