The field of elderly care and movement analysis is witnessing significant advancements with the integration of artificial intelligence (AI) and deep learning techniques. Researchers are focusing on developing innovative methods for automated assessment of physical independence, fall detection, and activities of daily living (ADL) recognition. These efforts aim to improve the dignity and independence of older adults, while also reducing the burden on healthcare professionals. Noteworthy papers in this area include: Movement-Specific Analysis for FIM Score Classification Using Spatio-Temporal Deep Learning, which proposes an automated FIM score estimation method using deep neural networks. Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics, which introduces a physics-informed deep learning framework for estimating muscle activations and forces across multi-joint systems. Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL, which envisions a next-generation elderly monitoring system that moves beyond fall detection toward ADL recognition. Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets, which highlights the need for improved representation of older adults in motion datasets. Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap, which proposes a dual-stream model that combines pose and biomechanical information for fall prediction.