Automatic Fall Detection and Activity Classification by a Wearable Camera

Abstract

Automated monitoring of everyday physical activities of elderly has come a long way in the past two decades. These activities might range from critical events such as falls requiring rapid and robust detection to classifying daily activities such as walking, sitting and lying down for long term prognosis. Researchers have constantly strived to come up with innovative methods based on different sensor systems in order to build a robust automated system. These sensor systems can be broadly classified into wearable and ambient sensors. Various vision and non-vision based sensors have been employed in the process. Most popular wearable sensors employ non-vision based sensors such as accelerometers and gyroscopes and have the advantage of not being confined to restricted environments. But resource limitations leave them vulnerable to false positives and render the task of classifying activities very challenging. On the other hand, popular ambient vision based sensors like wall mounted cameras which have resource capabilities for better activity classification are confined to a specific monitoring environment and by nature raise privacy concerns. Recently, integrated wearable sensor systems with accelerometers and camera on a single device have been introduced wherein the camera is used to provide contextual information in order to validate the accelerometer readings. In this chapter, a new idea of using a smart camera as a waist worn fall detection and activity classification system is presented. Therefore, a methodology to classify sitting and lying down activities with such a system is introduced in order to further substantiate the concept of event detection and activity classification with wearable smart cameras.