In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven standardized postures. Those neural networks were then exported to a mobile application to achieve a real-time classification of the standardized postures. Results showed a real-time classification of 93.4% for eight standardized postures, even for users that experimented for the first-time our intelligent chair. Using the same mobile application they devised and implemented two correction algorithms, acting due to conformation change of the bladders in the chair's seat when a poor seating posture is detected for certain periods of time.
Learning and real-time classification of hand-written digits with spiking neural networks