Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions
Name
sensors-23-06789-v2.pdf
Size
5.74 MB
Format
Adobe PDF
Checksum (MD5)
8a9b8ea49139f29bf93174e83a16ff67
Author(s)
Bhave, Anushka
van Delden, Josephine
Gloor, Peter A.
Renold, Fritz K.
Date Issued
July 29, 2023
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 23 (15): 6789 (2023)
Version
Final published version
Abstract
This paper presents novel 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. We have developed a real-time software to calculate the physical synchronicity of team members by tracking the difference in arm, leg, and head movements using Lightweight OpenPose. 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 1-D CNNs to predict the collective emotions of the musicians. The model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s23156789
