Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL

TL;DR

This paper discusses the development of privacy-preserving, edge-deployed AI systems for comprehensive elderly activity monitoring, moving beyond fall detection to recognize daily routines and support independence.

Contribution

It introduces a framework for next-generation ADL recognition using federated learning and edge deployment, with initial experiments on fall detection as a proxy task.

Findings

Feasibility demonstrated with SISFall dataset and GAN-augmented variants.

Initial federated learning results under non-IID conditions.

Successful embedded deployment on Jetson Orin Nano devices.

Abstract

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.