Human Activity Recognition with Consumer Devices and Real-Life Perspectives

Abstract

During the last decade, research on human activity recognition has grown due to its applications in diverse fields such as video surveillance, exercise monitoring or health monitoring systems. In the latter case, researchers are putting their efforts into using human activity recognition in monitoring elderly people, for example, for fall prevention and detection applications. Existing research usually has drawbacks regarding their requirements regarding sensing devices (e.g., cost, quantity, location). Therefore, research needs to keep these drawbacks in mind to have a real impact on society. This thesis addresses the abovementioned issue by focusing on the feasibility of the use of consumer devices such as smartphones and smartwatches, and cheap devices like microcontrollers, for human activity recognition and its application in real-life problems.