Sitting Posture Detection using Fuzzy Logic Development of a Neuro-fuzzy Algorithm to Classify Postural Transitions in a Sitting Posture

摘要

In a previous work, a chair prototype was used to detect 11 standardized seating 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 a new classification algorithm, which was developed in order to classify the postures using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. This new Posture Classification Algorithm is based on Fuzzy Logic and is able to determine if the user is adopting a good or a bad posture for specific time periods. The newly developed Classification Algorithms will prompt the improvement of new Posture Correction Algorithms based on Fuzzy Actuators.