Smart homes offer great convenience for people living alone and assistance for physically impaired inhabitants. Robust behavioural analysis technology is one of the keys to maximizing the role of the Smart Home. Typically, when it comes to the behavioural analysis of its inhabitants, most researchers have acquired it through data collection from sensors, cameras, and portable Bluetooth sensors. However, a gap in research exists concerning activity recognition in the context of the users physical location in the environment. In this paper, we propose a hierarchical framework based on Hidden Markov Model (HMM) and suggest dividing the behavioural sequence analysis into two layers: spatial transfer and sensor transfer. In addition, we apply probabilistic model checking to verify the properties of each module’s state transfer and obtain the probability of occurrence of the corresponding behavioural sequence. By integrating an implicit Markov model and probabilistic model checking, we effectively analyse the composition and probability of occurrence of three arbitrary sequences of complex behaviours. Finally, anomaly detection and behavioural guidance are discussed based on the proposed behavioural analysis methods
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China (No. 61976130 , 62206227 ), the Chengdu International Science Cooperation Project under Grant 2020-GH02-00064-HZ , and China Scholarship Council, China .
© 2023 Elsevier B.V.
- Smart home
- Behavioural analysis
- Hidden Markov model
- Probabilistic model checking