Authors: Sook-Ling Chua; Lee Kien Foo; Saed Sa'deh Suleiman Juboor
Addresses: Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia ' Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia ' Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
Abstract: Many supervised methods have been proposed to infer the particular activities of the inhabitants from a variety of sensors attached in the home. Current activity recognition systems either assume that the sensor stream has been presegmented or use a sliding window for activity segmentation. This makes real-time activity recognition task difficult due to the presence of temporal gaps between successive sensor activations. In this paper, we propose a method based on a set of hidden Markov models that can simultaneously solve the problem of activity segmentation and recognition on streaming sensor data without relying on any sliding window methods. We demonstrate our algorithm on sensor data obtained from two publicly available smart homes datasets.
Keywords: real-time; activity recognition; activity segmentation; streaming data; hidden Markov model; HMM; smart home.
International Journal of Advanced Intelligence Paradigms, 2020 Vol.15 No.2, pp.146 - 164
Received: 01 Aug 2016
Accepted: 15 Dec 2016
Published online: 14 Feb 2020 *