Intelligent Chair Sensor: Classification of sitting posture

Leonardo Martins, Rui Lucena, João Belo, Marcelo Santos, Cláudia Quaresma, Adelaide P. Jesus, Pedro Vieira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


In order to build an intelligent chair capable of posture detection and correction we developed a prototype that gathers the pressure map of the chair's seat pad and backrest and classifies the user posture and changes its conformation. We gathered the pressure maps for eleven standardized postures in order to perform the automatic posture classification, using neural networks. First we tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven postures. Those neural networks were exported to a mobile application in order to do real-time classification of those postures. Results showed a real-time classification of around 70% for eleven standardized postures, but we improved the overall classification score to 93.4% when we reduced the posture identification to eight postures, even when this classification was done with unfamiliar users to the posture identification system.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 14th International Conference, EANN 2013, Proceedings
Number of pages10
Volume383 CCIS
EditionPART 1
ISBN (Print)9783642410123
Publication statusPublished - 2013
Event14th International Conference on Engineering Applications of Neural Networks, EANN 2013 - Halkidiki, Greece
Duration: 13 Sep 201316 Sep 2013

Publication series

NameCommunications in Computer and Information Science
NumberPART 1
Volume383 CCIS
ISSN (Print)18650929


Conference14th International Conference on Engineering Applications of Neural Networks, EANN 2013


  • Neural Networks
  • Posture Classification
  • Posture correction
  • Pressure-distribution sensors
  • Sensing chair
  • Sitting posture


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