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Unlike athletes, musicians receive little or no education regarding the most effective ways to prepare their bodies and minds for the high demands of performing music. The SAMUSE project addresses this shortcoming in training and educating musicians. It will lay the foundations of a research-based professionalization of instrumental music education by developing innovative quantitative methods informed by Artificial Intelligence (AI) modeling strategies to characterize different skill levels in music performance. Such characterization is fundamental to understanding the music skill acquisition continuum and to deploying this understanding for the design of adequate and effective learning paths.


  To this end, the SAMUSE project will introduce an integrative (multi-sensing measurements) and development-oriented (multiple skill levels) approach to the study of skill acquisition in music performance. It will elaborate a solid theoretical and methodological framework to identify and define the psychophysiological parameters (“embodiment indicators”) that change over time in relation to the different skill acquisition levels. By conducting a focused cross-sectional study, the physical, cognitive, and emotional engagement of musicians will be measured at different skill levels. The project will contribute to novel measurement and analysis methods in music performance research, allowing a more advanced and sophisticated characterization of skill levels in music performance, among other project outcomes. In addition, we will focus on the development of wearable and flexible sensors, which is a noteworthy research area that has captured the interest of numerous researchers. These sensors have the potential to be seamlessly incorporated into clothing, gloves, and accessories. For music performance research, wearable and flexible sensors could be designed to capture a wide range of data, including gestures and physiological responses.

  The project will support further research on the design and evaluation of novel educational approaches (e.g., through longitudinal intervention studies), and on the development of advanced digital monitoring applications to enhance healthy and effective performance skill acquisition. This will ultimately support several research applications, such as quality enhancement in music education, multimodal sensor fusion algorithms, and user models. From an engineering standpoint, the project's objective is to create innovative, smart, wearable, and flexible sensors designed for data collection during music performance. These sensors would prioritize user comfort, ease of use, efficiency, and environmental friendliness.

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