Detection Elder Abnormal Activities by using Omni-directional Vision Sensor: Activity Data Collection and Modeling

One of the most important aspects of gerontechnology is to support the elder who lives alone at home. According to the elder´s relatively obvious and stable routine of daily activities, this paper proposes a statistical approach based on machine vision to build an elder indoor and outdoor activity model (EIOAM) through analysis of the elder´s daily activity data in the main activity places and the entry/exit places collected by the omni-directional vision sensor (ODVS). Since the elder´s daily routine varies with the alternating of seasons and growing of age, the model should learn the routine of activities adaptively. This model is able to detect and predict the abnormal activities of the elder through detecting the significant deviations of the activity data in spatial and temporal aspects. By using this model, one can not only detect the abnormal activities happened in sight of the vision sensor (indoor), but also out of sight (outdoor). Thus provides a new methodology for the remote home care of the elder who lives alone