@inproceedings{e923323a84b24a1583e0f7b3ca608ab7,
title = "Lightweight Network Based Real-time Anomaly Detection Method for Caregiving at Home",
abstract = "Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in Shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection.",
keywords = "aging in place, anomaly detection, elderly care, fall detection, healthcare, keypoint detection, lightweight network, video",
author = "Bin Wang and Xingjiao Wu and Miaomiao Gong and Jin Zhao and Yuling Sun",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022 ; Conference date: 04-05-2022 Through 06-05-2022",
year = "2022",
doi = "10.1109/CSCWD54268.2022.9776035",
language = "英语",
series = "2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1323--1328",
booktitle = "2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022",
address = "美国",
}