Skip to main content

A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare.

Wang, Y., Wang, X., Arifoglu, D., Lu, C., Bouchachia, A., Geng, Y. and Zheng, G., 2023. A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare. Electronics, 12 (7), 1539.

Full text available as:

electronics-12-01539.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.3390/electronics12071539


With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.

Item Type:Article
Additional Information:(This article belongs to the Special Issue Graph Machine Learning)
Uncontrolled Keywords:ambient sensors; healthcare; abnormal behaviour detection
Group:Faculty of Science & Technology
ID Code:38703
Deposited By: Symplectic RT2
Deposited On:15 Jun 2023 10:01
Last Modified:15 Jun 2023 10:01


Downloads per month over past year

More statistics for this item...
Repository Staff Only -