Optimization of sitting posture classification based on user identification

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%.