A Model for Sitting Postures in relation to Learning and Non-learning Behaviors

Abstract

As more and more information find their way to the internet, people are able to do more at their own desk than ever before, all in the comfort of a private environment. But as more activities, especially learning, are able to be done through the personal desktop space, the question is then raised of whether or not one is really engaged and/or learning and not being distracted by other things that the internet offer. For this, we propose a model that will associate various sitting postures with a person's level of engagement and/or learning. Said model will know what kind of postures usually indicate a state of engagement to a person's work and learning, and which postures indicate a falling out from that state. We apply machine learning techniques to a database of silhouette images, captured using a Microsoft Kinect, in order to extrapolate patterns that would help link a user's postures to his learning state. Our model can be used to assist users regain learning postures and suggest for a change of activity if prolonged periods of non-learning are detected so that users will gain the most out of their time.