Abstract
Abstract There has been a lot of work focussing on activity recognition in smart homes, with the aim of the home being to monitor the activities of the inhabitant and identify deviations from the norm. For a smart home to support its inhabitants, the recognition sys- tem needs to accurately learn from the observations acquired through sensors, which are installed in the home. Given a predefined set of the inhabitant's daily activities, the question is which sensors are important to accurately recognise these activities. This paper addresses the sensor selection problem through a filter-based approach, which is based on information gain. We evaluate the effectiveness of the proposed method on two publicly available smart home datasets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
