@inproceedings{618235fac4744a87af6634e2b7bbfae0,
title = "Detecting abnormal patterns of daily activities for the elderly living alone",
abstract = "In order to reduce the potential risks associated with physically and cognitively impaired ability of the elderly living alone, in this work, we develop an automated method that is able to detect abnormal patterns of the elderly's entering and exiting behaviors collected from simple sensors equipped in home-based setting. With spatiotemporal data left by the elderly when they carrying out daily activities, a Markov Chains Model (MCM) based method is proposed to classify abnormal sequences via analyzing the probability distribution of the spatiotemporal activity data. The experimental evaluation conducted on a 128-day activity data of an elderly user shows a high detection ratio of 92.80% for individual activity and of 92.539% for the sequence consisting of a series of activities.",
keywords = "Abnormal Pattern, Infrared Tube, MCM, Spatiotemporal",
author = "Tingzhi Zhao and Hongbo Ni and Xingshe Zhou and Lin Qiang and Daqing Zhang and Zhiwen Yu",
year = "2014",
doi = "10.1007/978-3-319-06269-3_11",
language = "英语",
isbn = "9783319062686",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "95--108",
booktitle = "Health Information Science - Third International Conference, HIS 2014, Proceedings",
note = "3rd International Conference on Health Information Science, HIS 2014 ; Conference date: 22-04-2014 Through 23-04-2014",
}