Training a classifier for activity recognition using body motion simulation

Classification of human activity is an increasingly popular topic, as it is employed in various fields from fitness to remote health monitoring. Current automated approaches based on wearable sensors typically use supervised learning methodologies, where a classifier is trained with experimental data. This paper proposes the use of body motion and sensor simulation for building, or extending, the training databases and improve the classifier accuracy, without requiring further experimental campaigns.