Bhave, A.; van Delden, J.; Gloor, P.A.; Renold, F.K. Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions. Sensors2023, 23, 6789.
Bhave, A.; van Delden, J.; Gloor, P.A.; Renold, F.K. Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions. Sensors 2023, 23, 6789.
Bhave, A.; van Delden, J.; Gloor, P.A.; Renold, F.K. Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions. Sensors2023, 23, 6789.
Bhave, A.; van Delden, J.; Gloor, P.A.; Renold, F.K. Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions. Sensors 2023, 23, 6789.
Abstract
This paper presents preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. Building upon previous studies that measured the physical synchronicity of team members by tracking their body movements and measuring the difference in arm, leg, and head movements, we introduce a novel metric termed "team entanglement". We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness and higher levels of joy among the musicians. Furthermore, we utilize a Convolutional Neural Network (CNN) based deep learning model to predict the collective emotions of the musicians. This model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
