Xiaodong Lin-Siegler, Benjamin J Lovett, Yiran Du, Kan Yamane, Keying Wang, Syntia Hadis
What experiences constitute failures? High school students' reflections on their struggles in STEM classes
Annals of the New York Academy of Sciences
emotion detection ed involves identification understanding individuals emotional state various cues facial expressions voice tones physiological changes behavioral patterns context behavioral analysis employed observe actions behaviors emotional interpretation work specifically employs behavioral metrics like drawing handwriting determine persons emotional state recognizing actions physical functions integrating motor cognitive processes study proposes attentionbased transformer model innovative approach identify emotions handwriting drawing samples thereby advancing capabilities ed domains fine motor skills artistic expression initial data obtained provides set points correspond handwriting drawing strokes stroke point subsequently delivered attentionbased transformer model embeds highdimensional vector space model builds prediction emotional state person generated sample integrating important components patterns input sequence using selfattentional processes proposed approach possesses distinct advantage enhanced capacity capture longrange correlations compared conventional recurrent neural networks rnn characteristic makes particularly wellsuited precise identification emotions samples handwriting drawings signifying notable advancement field emotion detection proposed method produced cuttingedge outcomes 9264 benchmark dataset known emothaw emotion recognition via handwriting drawing