Fall Detection and Activity Recognition with Machine Learning

Due to the rapid aging of the European population, an effort needs to be made to ensure that the elderly can live longer independently with minimal support of the working-age population. The Confidence project aims to do this by unobtrusively monitoring their activity to recognize falls and other health problems. This is achieved by equipping the user with radio tags, from which the locations of body parts are determined, thus enabling posture and movement reconstruction. In the paper we first give a general overview of the research on fall detection and activity recognition. We proceed to describe the machine learning approach to activity recognition to be used in the Confidence project. In this approach, the attributes characterizing the user's behavior and a machine learning algorithm must be selected. The attributes we consider are the locations of body parts in the reference coordinate system (fixed with respect to the environment), the locations of body parts in a body coordinate system (affixed to the user's body) and the angles between adjacent body parts. Eight machine learning algorithms are compared. The highest classification accuracy of over 95 % is achieved by Support Vector Machine used on the reference attributes and angles.