Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL
It addresses the need for privacy-preserving and dignified elderly care in aging societies, though it is incremental as it builds on existing fall detection methods toward a broader vision.
This position paper tackles the problem of elderly monitoring by proposing a shift from fall detection to comprehensive Activities of Daily Living (ADL) recognition, demonstrating initial feasibility through experiments with the SISFall dataset and federated learning on Jetson Orin Nano devices.
This position paper envisions a next-generation elderly monitoring system that moves beyond fall detection toward the broader goal of Activities of Daily Living (ADL) recognition. Our ultimate aim is to design privacy-preserving, edge-deployed, and federated AI systems that can robustly detect and understand daily routines, supporting independence and dignity in aging societies. At present, ADL-specific datasets are still under collection. As a preliminary step, we demonstrate feasibility through experiments using the SISFall dataset and its GAN-augmented variants, treating fall detection as a proxy task. We report initial results on federated learning with non-IID conditions, and embedded deployment on Jetson Orin Nano devices. We then outline open challenges such as domain shift, data scarcity, and privacy risks, and propose directions toward full ADL monitoring in smart-room environments. This work highlights the transition from single-task detection to comprehensive daily activity recognition, providing both early evidence and a roadmap for sustainable and human-centered elderly care AI.