Optimization of sitting posture classification based on user identification

Abstract

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%.

Designing of smart chair for monitoring of sitting posture using convolutional neural networks

“…Shukor et al (2018) classified the eight poses by attaching sensors to the seat and backrest, and their accuracy was 90 percent. Ribeiro et al (2015) were equipped with eight sensors, four each on the seat and backrest. They adopted ANN as a classification algorithm and showed accuracy of 89.0 percent for 12 postures.…”

Detection and classification of islanding and power quality disturbances in microgrid using hybrid signal processing and data mining techniques

“…Based on (18)- (20), the imagery part [18] of z Y, y at (Y − 1)/2, i.e. X Y, y [(Y − 1)/2] is the HT and computed by…”

“…The selection of the stopping criterion is formulated in such a manner that each infected node can split a high of ten annotations. The most appropriate condition formulated at each individual node in this paper utilises the Gini's diversity index [20]. Division between events rather than node miscellany is made possible for multiclass classification, using the towing rule base.…”

“…The accuracy of the proposed method significantly depends on the selection of an accurate window. The voltage and current signal samples at the target DG terminals of a standard IEC microgrid model [20] in MATLAB/Simulink are collected for a number of operating conditions, which are processed through the proposed MSMGF and STMHT techniques, respectively, in order to detect the occurrence of the events. Some simple computations over the outputs of the proposed MSMGF and STMHT techniques, respectively, generate the target parameter set for the microgrid subjected to a large number of multiclass power system disturbances like islanding, switching faults and power quality events such as voltage sag, voltage swell and capacitor bank switching, harmonic load switching, balanced and unbalanced load switching, flicker and so on.…”