Anomaly detection in smart houses: Monitoring elderly daily behavior for fall detecting

Abstract

Smart Houses and Internet of Things (IoT) are two present tendencies in our days. Due to these technologies, the existent types of equipment in a smart house (sensors, thermostats, and video cams) allow us to analyze and collect data from a person's daily activities and use it in the field of anomaly detection. Therefore, noninvasive monitoring techniques can be applied to people's residences. When focusing on the elderly population, this type of approach can be used to detect and report a fall, decreasing the costs of monitoring these individuals. This paper uses images from a Microsoft Kinect cam, accelerometers' data, digital image processing and computer vision techniques to make a comparative study between different supervised classifiers and statistic approaches when they are being used in the fall detection problem. The results show that some of the tested classifiers are efficient in this task, reaching an accuracy of 96.67% and 98.79%.