Abstract:
Population aging has become a worldwide problem. Falls are considered as the first source of disabilities among elderly people. Fall detection algorithms are the key to d...Show MoreMetadata
Abstract:
Population aging has become a worldwide problem. Falls are considered as the first source of disabilities among elderly people. Fall detection algorithms are the key to distinguish a fall from daily activities, automatically alert when a fall occurred and significantly decrease the time of rescue when the monitored patient falls down. The algorithm presented in this paper uses tri-axial accelerometer outputs to discriminate between falls and daily activities. It is mainly based on a two-thresholds approach and inactivity posture recognition after falling. The algorithm showed prominent results compared to existing works and will be improved and implemented on a Zynq board for future applications.
Published in: 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)
Date of Conference: 01-03 June 2016
Date Added to IEEE Xplore: 25 August 2016
ISBN Information:
Electronic ISSN: 2151-1357