Authors:

Joseph Issac Getachew

Date Created:

Not specified

Course Title:

Professor:

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About Paper:

This research project involves developing a comprehensive mobile app that's aimed at evaluating and classifying a musicians performance. This is done using computer vision and machine learning techniques. Our app addresses the need for real-time posture evaluation in order to prevent long term effects on the body, as well as improving a musicians performance. In our research team, we are divided up into different groups like hands, bow, and UI. This is all under the evaluator category, but specifically the hands team using methods like advanced machine learning models to identify various postures. My contributions to this project have been implementing a shoulder classification function that accurates identifies shoulder levelness. I have also made attempts at reducing noise for all our classifiation models using a sliding window technique, and have contributed to the low elbow identification. The shoulder classification looks at pose estimation data to see its elevation and alignment patterns to then identify shoulder levelness. These classification functions are an integral part of the research project as they allow for immediate feedback on a musicians posture. This will enable correction for bad posture improving health and performance. Our research is contributing to the field of technology-assisted musical training and injury prevention. Keywords: [no keywords provided]

Source:

Purdue University / 2025

Topics:

No topics listed

Co-authors:

Joseph Issac Getachew