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