Real-Time Posture Classification and Correction based on a Neuro-Fuzzy Control System Leonardo Martins1,2, Hugo Pereira1, Rui Almeida1, Bruno Ribeiro1, Ana Gabriel1, Cláudia Quaresma1,3, Adelaide Jesus1 and Pedro Vieira1,3 1 Department of Physics, Faculty of Sciences and Technology New University of Lisbon, Caparica, Portugal 2 UNINOVA, Institute for the Development of New Technologie, Caparica, Portugal 3 LIBPhys-UNL, Department of Physics, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa, 2829-516 Monte da Caparica, Portugal {l.martins, p110595}@campus.fct.unl.pt, rui.almeida@ngns-is.com, {bmf.ribeiro, a.gabriel}@campus.fct.un.pt, {q.claudia, maj, pmv}@fct.unl.pt Introduction Nowadays, due to a rapid technological development, automation and computerization of the workplace, sitting has become the most common posture in developed countries (Chau et al. 2010; Hartvigsen et al. 2000). However, assuming a poor sitting posture leads to several health problems, namely back, shoulder and neck pain (Graf et al. 1995). In a previous work, an intelligent chair was developed and was shown to classify and correct the seating position (Martins et al. 2014; Lucena et al. 2012). Improvements on this intelligent chair prototype are described in this work, culminating with the development of a new prototype and better posture classification and correction algorithms based on Artificial Neural Networks (ANN) and Fuzzy Logic. The new prototype In the second prototype, the air bladders were designed using CAD software and industrially manufactured (instead of the previous manually built bladders). In this new prototype each one of the eight bladders is connected to its own control module, connected by a single-board computer, the Raspberry-Pi. A Bluetooth interface was added in order to connect it to the outside world, enabling one to retrieve data and statistics through a smartphone. SYPEC – System for Posture Evaluation and Correction Pressure Mapping Two matrices of 2 by 2 air bladders attached to a piezoelectric gauge pressure sensor were placed in the seat pad and in the back rest in order to get pressure maps for the current user posture. Posture Evaluation Classification algorithms evaluate the user’s posture by analyzing the pressure maps in real time Second Prototype First Prototype If a poor posture is detected, a correction algorithm will change the chair’s conformation by inflating or deflating the air bladders in order to increase disconfort and encourage the user to change his position. Posture Correction Classification of Sitting Posture In order to classify 12 different postures, the pressure maps (Figure) were used as input for an ANN. For the parameterization of the ANN we used a combination of 1 layer, 40 neurons and resilient back propagation algorithm as the network training function. We obtained an overall posture classification of 80.9%. Fuzzy Logic was introduced to the existing Algorithm by using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. Correction of Sitting Posture This new Neuro-Fuzzy Algorithm now takes into account intermediate postures between the previous standard and the time period adopted in each posture, prompting the development of a Fuzzy Control System that inflates and deflates each bladder during a specific period of time according to the Fuzzy Output. References Chau, J.Y. et al., 2010. Are workplace interventions to reduce sitting effective ? A systematic review. Preventive Medicine, 51(5), pp.352–356. Hartvigsen, J. et al., 2000. Is sitting-while-at-work associated with low back pain? A systematic , critical literature review. Scand J Public Health, 28(3), pp.230–239. Graf, M., Guggenbuhl, U. & Krueger, H., 1995. An assessment of seated activity and postures at five workplaces. International Journal of Industrial Ergonomics, 15(2), pp.81–90. Lucena, R. et al., 2012. INTELLIGENT CHAIR SENSOR-ACTUATOR - A Novel Sensor Type for Seated Posture Detection and Correction. In Proceedings of the International Conference on Biomedical Electronics and Devices. SciTePress - Science and and Technology Publications, pp. 333–336. Martins, L. et al., 2014. Intelligent Chair Sensor: Classification and Correction of Sitting Posture. International Journal of System Dynamics Applications, 3(2), pp.65–80.