Detecting Abnormal Patterns of Daily Activities for the Elderly Living Alone
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 elderlys 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.
Abnormal Pattern Infrared Tube Spatiotemporal MCM
Tingzhi Zhao Hongbo Ni Xingshe Zhou Lin Qiang Daqing Zhang Zhiwen Yu
School of Computer Science, Northwestern Polytechnic University, China Handicom Lab., Institut Telecom SudParis, France
国际会议
The Third International Coference on Health Information Science(HIS2014)2014年第三届健康信息学国际学术会议
深圳
英文
95-108
2014-04-22（万方平台首次上网日期，不代表论文的发表时间）