从跌倒检测迈向日常活动识别，守护老人尊严的智能监护新范式
Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL
- 基于生成对抗网络增强数据，以跌倒检测为切入点验证可行性
- 在非独立同分布条件下实现联邦学习，成功部署于边缘设备Jetson Orin Nano
- 聚焦隐私保护与真实场景挑战，为未来全周期活动监控指明方向
本文构想下一代老年人监护系统，从单一跌倒检测拓展至日常生活活动（ADL）识别。目标是构建隐私保护、边缘部署、联邦学习的AI系统，以稳健识别并理解日常行为，支持老年群体独立与尊严。当前ADL专用数据集仍在收集中，本研究以SISFall数据集及其GAN增强版本为实验基础，将跌倒检测作为代理任务进行初步验证。实验结果显示，在非独立同分布条件下可实现联邦学习，并成功嵌入Jetson Orin Nano边缘设备。文章进一步提出领域偏移、数据稀缺与隐私风险等开放挑战，展望在智能房间环境中实现完整ADL监控的路径。本工作标志着从单任务检测向全面日常活动认知的转变，提供了早期实证与可持续、以人为本的老年照护AI发展蓝图。
原文摘要 · Abstract (English)
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.
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