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 seated activities for a human-computer interface. Our system design displays accurate near-real-time classification performance on data from subjects on which the posture recognition system was not trained by using a set of carefully designed, subject-invariant signal features. By using a near-optimal sensor placement strategy, we keep the number of required sensors low thereby reducing cost and computational complexity. We evaluated the performance of our technology using a series of empirical methods including (1) cross-validation (classification accuracy of 87\% for ten postures using data from 31 sensors), and (2) a physical deployment of our system (78\% classification accuracy using data from 19 sensors).
machine learning ubiquitous computing haptic interfaces sensor technologies personal technologies