Human Activity Recognition with Wearable Sensors

This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information – others include the user’s lo-cation or the state of his environment – which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors – mainly ac-celerometers – to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly use