Abstract
In a previous work, a chair prototype was used to detect 11 standardized siting postures of users, using just 8 air bladders (4 in the chair’s seat and 4 in the backrest) and one pressure sensor for each bladder. In this paper we describe the development of a new prototype, which is able to classify 12 standard postures with an overall score of 80.9 % (using a Neural Network Algorithm). We tested how this Algorithm worked during postural transitions (frontal and lateral flexion) and in intermediate postures, identifying some limitation of this Algorithm. This prompted the development of a Posture Classification Algorithm based on Fuzzy Logic and is able to determine if the user is adopting a good or a bad posture for specific time periods, using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. This newly developed Classification Algorithms is advancing the development of new Posture Correction Algorithms based on Fuzzy Actuators.
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Acknowledgements
This project (QREN 13330 – SYPEC) is supported by FEDER, QREN – Quadro de Referência Estratégico Nacional, Portugal 07/13 and PORLisboa – Programa Operacional Regional de Lisboa. The authors wish to thank Eng. Pedro Duque, Eng. Rui Lucena, Eng. João Belo and Eng. Marcelo Santos for the help provided in the construction of the first prototype.
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Martins, L. et al. (2015). Real-Time Fuzzy Monitoring of Sitting Posture: Development of a New Prototype and a New Posture Classification Algorithm to Detect Postural Transitions. In: Fred, A., Gamboa, H., Elias, D. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2015. Communications in Computer and Information Science, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-27707-3_26
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DOI: https://doi.org/10.1007/978-3-319-27707-3_26
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