Detecting Abnormal Behaviors in Smart Home

Yu, Yijun; Li, Changyang; Jonas, Mainuddin Ahmad; Ma, Chuanhao; Shezan, Faysal Hossain; Shen, Siyuan; Gao, Peng; Yuan, Tian

doi:10.1109/massw.2019.00014

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Although previous approaches towards behavioral monitoring using smart home technology were focused on collecting detailed information about health status and specific activity patterns of individuals, they ultimately proved to be obstructive, as they were limited to a small number of devices, dependent on participants to report the predefined tasks, time-consuming, and not generalizable for other smart homes [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Jalali¹,

Sahu²,

Oetomo³

et al. 2020

JMIR Mhealth Uhealth

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Background One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.

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Section: Introductionmentioning

confidence: 99%

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Jalali¹,

Sahu²,

Oetomo³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

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

Wang

Arifoǧlu

et al. 2023

Electronics

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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.

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Detecting Abnormal Behaviors in Smart Home

Cited by 2 publications

References 9 publications

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

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

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