Patterns of contact and communication in scientific research collaboration
CSCW '88 Proceedings of the 1988 ACM conference on Computer-supported cooperative work
Leonardo's Laptop: Human Needs and the New Computing Technologies
Leonardo's Laptop: Human Needs and the New Computing Technologies
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Conversational scene analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Sensing and modeling human networks
Sensing and modeling human networks
Automatic prediction of frustration
International Journal of Human-Computer Studies
Supporting creativity with awareness in distributed collaboration
Proceedings of the 2007 international ACM conference on Supporting group work
Journal of Cognitive Neuroscience
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Face Recognition Based on PCA and LDA Combination Feature Extraction
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Group reactions to visual feedback tools
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Automated language-based feedback for teamwork behaviors
Automated language-based feedback for teamwork behaviors
Predicting creativity in the wild: experience sampling method and sociometric modeling of movement and face-to-face interactions in teams
Facilitating TV production using StoryCrate
Proceedings of the 9th ACM Conference on Creativity & Cognition
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Relationships between creativity in teamwork, and team members' movement and face-to-face interaction strength were investigated "in the wild" using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, in academic and industry settings. Activities (movement and face-to-face interaction) and creativity of one five-member and two seven-member teams were tracked for twenty-five days, eleven days, and fifteen days respectively. Paired-sample t-test confirmed average daily movement energy during creative days was significantly greater than on non-creative days and that face-to-face interaction tie strength of team members during creative days was significantly greater than for non-creative days. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data yielded a model that predicted creativity with 87.5% and 91% accuracy, respectively. Computational models that predict team creativity hold particular promise to enhance Creativity Support Tools.