Intelligent chair sensor: classification and correction of sitting posture

Abstract

In order to build an intelligent chair capable of posture detection and correction we developed a prototype that measures a pressure map of the chair's seat pad and backrest and classifies the user posture. The posture classification was done using neural networks that were trained for 5 standardized postures achieving an overall classification of around 98%. Those neural networks were exported to a mobile application in order to do real-time classification of those postures. Using the same mobile application we devised two correction algorithms that were implemented in order to create an intelligent chair capable of posture detection and correction. The posture correction is forced through the change of the conformation of the chair's seat and backrest by changing the pressure of eight pneumatic bladders.