Abstract
We monitor activities of daily living of smart home residents to detect anomalies in their behavior. Unlike traditional anomaly detection systems, we aim to reduce false positives in anomaly detection with the help of semantic rules. Some of these rules are predefined based on expert knowledge and the rest are learned by the system with the help of resident/expert feedback. We also correlate trend of change in different activities to improve anomaly detection. In addition to monitor statistical deviation from regular behavior, we also detect deviation from healthy and social norms (defined by experts) as anomalies.
Sign-in/Register to access full text options 
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.
