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.

翻译：本立场文件展望了一种超越跌倒检测、以实现更广泛的日常生活活动（ADL）识别为目标的下一代老年人监护系统。我们的最终目标是设计能够稳健检测和理解日常作息、支持老龄化社会中独立性与尊严的隐私保护、边缘部署及联邦人工智能系统。目前，针对ADL的专用数据集仍在收集中。作为初步步骤，我们通过使用SISFall数据集及其GAN增强变体的实验，将跌倒检测作为代理任务，论证了系统可行性。我们报告了在非独立同分布条件下联邦学习的初步结果，以及在Jetson Orin Nano设备上的嵌入式部署情况。随后，我们阐述了领域偏移、数据稀缺性和隐私风险等开放挑战，并提出了在智能房间环境中实现完整ADL监测的发展方向。本研究强调了从单任务检测向全面日常活动识别的转变，为可持续且以人为本的老年人照护人工智能提供了早期证据和技术路线图。