A supervised learning approach for behaviour recognition in smart homes

Article type: Research Article

Authors: Chua, Sook-Ling^{a; *} | Marsland, Stephen^b | Guesgen, Hans^b

Affiliations: [a] Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia. E-mail: [email protected] | [b] School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: One application of Ambient Intelligence (AmI) that supports people in their daily activities is the smart home, which has become a popular topic for research over the past 10 years. The smart home can support the inhabitant in a variety of ways, such as watching for potential risks, detecting any abnormality, adapting the home for environmental conditions and inducing behavioural change. This often requires the smart home to recognise the behaviours of the inhabitant. In this paper, we introduce a method that can accurately recognise the inhabitant’s behaviours. This includes both the segmentation of the sensor stream and the identification of behaviours. We demonstrate our algorithm on sensor data from real smart homes.

Keywords: Behaviour recognition, activity segmentation, hidden Markov model, smart home

DOI: 10.3233/AIS-160378

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 8, no. 3, pp. 259-271, 2016