MIT Center for Collective Intelligence, Cambridge, MA 02142, USA.
Shanti Music Productions Renold & Co., 5012 Schönenwerd, Switzerland.
Sensors (Basel). 2023 Jul 29;23(15):6789. doi: 10.3390/s23156789.
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%.
本文提出了一项新颖的初步研究，旨在调查通过多人姿态同步量化的一群爵士音乐家的流动与他们的集体情绪之间的关系。我们已经开发了一个实时软件，通过使用 Lightweight OpenPose 跟踪手臂、腿部和头部运动的差异来计算团队成员的身体同步性。我们采用面部表情识别来评估音乐家的集体情绪。通过相关和回归分析，我们确定更高水平的身体和头部运动同步性与音乐家的更低水平的厌恶、愤怒、悲伤和更高水平的喜悦相对应。此外，我们利用 1-D CNN 来预测音乐家的集体情绪。该模型利用 17 个身体同步关键点向量作为特征，训练准确率为 61.47%，测试准确率为 66.17%。
