Research Article
Improving classification of posture based attributed attention assessed by ranked crowd-raters
@ARTICLE{10.4108/icst.pervasivehealth.2015.259171, author={Patrick Heyer and Jesus Rivas and Luis Sucar and Felipe Orihuela-Espina}, title={Improving classification of posture based attributed attention assessed by ranked crowd-raters}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={1}, number={2}, publisher={IEEE}, journal_a={PHAT}, year={2015}, month={8}, keywords={attention, adaptation, posture, neurorehabilitation, semi-supervised learning}, doi={10.4108/icst.pervasivehealth.2015.259171} }- Patrick Heyer
Jesus Rivas
Luis Sucar
Felipe Orihuela-Espina
Year: 2015
Improving classification of posture based attributed attention assessed by ranked crowd-raters
PHAT
EAI
DOI: 10.4108/icst.pervasivehealth.2015.259171
Abstract
Attribution of attention from observable body posture is plausible, providing additional information for affective computing applications. We previously reported a promissory $69.72\pm10.50$ ($\mu\pm \sigma$) of F-measure to use posture as a proxy for attributed attentional state with implications for affective computing applications. Here, we aim at improving that classification rate by reweighting votes of raters giving higher confidence to those raters that are representative of the raters population. An increase to $75.35\pm11.66$ in F-measure was achieved. The improvement in predictive power by the classifier is welcomed and its impact is still being assessed.
Copyright © 2015 P. Heyer et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.