Description:
This project aims to measure and model the comfort level of trombonists during instrument practice using a combination of biological signals (ECG, HRV, galvanic skin response, respiration, eye movements) and non-biological signals (instrumental audio, motion capture, inertial sensors). The main objective is to infer a latent comfort state from these multiple data streams, validated through questionnaires and stress indicators.
To understand the factors affecting comfort, the study manipulates variables such as room reverberation, instrument variations, exercise difficulty, and audience feedback. The methodology integrates signal processing, sensor data acquisition, and statistical modeling, leveraging both engineering expertise and musical practice.
The outcomes of this research will provide a framework for monitoring comfort during task performance, with applications in musical training and potentially in industrial settings, where a worker performing a task could be monitored in a similar way. This project bridges engineering, neuroscience, and human performance studies.
IDMIL Participants:
External Participants:
Thierry Dutoit (UMons – Belgium)
Laurence Ris (UMons – Belgium)