Robust, Low-Cost, Non-Intrusive Sensing and Recognition of Seated Postures

In this paper, we present a methodology for recognizing seated postures using data from pressure sensors installed on a chair. Information about seated postures could be used to help avoid adverse effects of sitting for long periods of time, or to predict a user’s activities as input to a humancomputer interface. Our approach to posture recognition avoids the use of expensive hardware and complex prediction algorithms while providing recognition for users, for whom the classifier is not trained, using a near-optimal sensor placement strategy. We evaluated the performance of our technology in a series of empirical evaluations including (1) cross-validation experiments (classification accuracy of 87% for ten postures), and (2) a physical deployment of our system (78% classification accuracy).