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

Abstract

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.

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

“…The authors performed a detailed exploration of various facets of IoT advancements specifically tailored to the medical field; however, their approach lacked specificity in addressing specific diseases, and did not emphasize how AMI could handle emergency cases. Moreover, the authors of [8] provided a detailed and comprehensive review of the techniques used to profile activities of daily living (ADL) and detect abnormal behavior for healthcare purposes. They defined abnormal behaviors and provided examples of abnormal behaviors/activities in the case of elderly people.…”

“…Outliers in text data are addressed using methods based on the Z-score or interquartile range (IQR). Additionally, windowing techniques can be applied to maintain time dependency in text datasets, with the size of the time window being either static or dynamic [8].…”

Ambient intelligent framework for modelling critical medical events based on context awareness

“…Hence, the facilitation of healthcare services during an emergency is often characterized by certain constraints that could be life-threatening [7]. The availability of wearable devices assists in capturing information associated with health statistics; however, the primary challenge is to disseminate the information on time by scaling the condition of emergency [8], [9]. All these challenges must be addressed precisely to incorporate the reliability of disseminating healthcare services during emergency conditions.…”