Optimization of sitting posture classification based on user identification

Bruno Ribeiro, Hugo Pereira, Rui Almeida, Adelaide Ferreira, Leonardo Martins, Cláudia Regina Pereira Quaresma, Pedro Vieira

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

6 Citations (Scopus)


In a precursory work, an intelligent sensing chair prototype was developed to classify 12 standardized sitting postures using 8 pneumatic bladders (4 in the chair's seat and 4 in the backrest) connected to piezoelectric sensors to measure inner pressure. A Classification of around 80% was obtained using Neural Networks. This work aims to demonstrate how algorithmic optimization can be applied to a newly developed prototype to improve posture classification performance. The aforementioned optimization is based on the split of users by sex and use two different previously trained Neural Networks (one for Male and the other for Female). Results showed that the best neural network parameters had an overall classification 89.0% (from the 92.1% for Female Classification and 85.8% for Male, which translates into an overall optimization of around 8%). Automatic separation of these sets was achieved with Decision Trees with an overall classification optimization of 87.1%.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 4th Portuguese Meeting on Bioengineering, ENBENG 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479982691
Publication statusPublished - 17 Apr 2015
Event2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG) - Porto, Portugal, Porto, Portugal
Duration: 26 Feb 201528 Feb 2015


Conference2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG)


  • Machine learning
  • Sensory intelligent chair
  • Sitting posture classification


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