Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Activity recognition systems learn from sensors readings to recognise activities of the occupants in a smart home. In order to recognise activities, the sensor stream has to be segmented before any classification can be carried out. Many methods handle segmentation and recognition separately. In this study, we propose a method that can segment and recognise activity simultaneously by using a set of trained hidden Markov models and Viterbi algorithm. We evaluate our proposed method on two publicly available smart home datasets.
How to cite this article:
Saed Sa`deh Juboor, Sook-Ling Chua and Lee Kien Foo. Simultaneous Activity Segmentation and Recognition in Smart Homes.
DOI: https://doi.org/10.36478/jeasci.2016.1851.1854
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2016.1851.1854