Abstract
Detecting abnormal activity is crucial in healthcare, especially for elderly people. Real time and early detection will prevent severe injuries and save lives. Time series data analysis can help to timely identify any abnormal behaviour outlier from daily routines. In this paper, we studied abnormal activity detection in healthcare applying machine learning and time series forecasting models and technology. A novel approach is proposed to detect abnormality in real time in consideration of risk factors in healthcare of elderly people. The approach is tested on real data set of a sensor hits and the locations of the sensor as well as descriptions outlining the types of sensors and the placements of the sensors. Experiment results show the effectiveness of the approach.
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.
