Improving classification of posture based attributed attention assessed by ranked crowd-raters

Abstract

Attribution of attention from observable body posture is plausible, providing additional information for affective computing applications. We previously reported a promissory 69. 72 ± 10. 50 (μ ± σ) 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 ± 11. 66 in F-measure was achieved. The improvement in predictive power by the classifier is welcomed and its impact is still being assessed.

Keywords

• attention
• adaptation
• posture
• neurorehabilitation
• semi-supervised learning