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

• URL: http://arxiv.org/abs/2511.11696v1
• Date: Wed, 12 Nov 2025 10:44:16 GMT
• Title: Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL
• Authors: Xun Shao, Aoba Otani, Yuto Hirasuka, Runji Cai, Seng W. Loke,
• Abstract summary: We envision a next-generation elderly monitoring system that moves beyond fall detection toward the broader goal of Activities of Daily Living (ADL) recognition.<n>Our ultimate aim is to design privacy-preserving, edge-deployed, and federated AI systems that can robustly detect and understand daily routines.
• Score: 2.6062709309204566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection [9.92596575679496]
  Internet of Things (IoT) is vulnerable to a wide range of cyber threats.<n>Intrusion detection systems (IDS) have been extensively studied to enhance IoT security.<n>We propose CITADEL, a self-supervised continual learning framework to extract robust representations from benign data.
  arXiv  Detail & Related papers  (2025-08-26T21:55:26Z)
• Privacy-aware IoT Fall Detection Services For Aging in Place [1.4061979259370276]
  Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050.<n>We propose a novel IoT-based Fall Detection as a Service framework to assist the elderly in living independently and safely by accurately detecting falls.<n>We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion.
  arXiv  Detail & Related papers  (2025-06-18T03:28:07Z)
• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
  We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD)<n>We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps.<n> Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-12-22T03:25:43Z)
• Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data [0.0]
  This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning framework.
  arXiv  Detail & Related papers  (2024-10-25T23:00:12Z)
• Semi-supervised Open-World Object Detection [74.95267079505145]
  We introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD) We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-02-25T07:12:51Z)
• Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review [0.6144680854063939]
  Many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data.
  arXiv  Detail & Related papers  (2024-01-22T09:40:52Z)
• Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities [50.231837687221685]
  Self-supervised learning (SSL) has transformed machine learning and its many real world applications. Unsupervised anomaly detection (AD) has also capitalized on SSL, by self-generating pseudo-anomalies.
  arXiv  Detail & Related papers  (2023-08-28T07:55:01Z)
• Weakly Supervised Anomaly Detection: A Survey [75.26180038443462]
  Anomaly detection (AD) is a crucial task in machine learning with various applications. We present the first comprehensive survey of weakly supervised anomaly detection (WSAD) methods. For each setting, we provide formal definitions, key algorithms, and potential future directions.
  arXiv  Detail & Related papers  (2023-02-09T10:27:21Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.