PREDICTING ARM MOVEMENTS FROM TORSO ORIENTATIONS: A SUPPORT VECTOR REGRESSION APPROACH

Victor TRIOHIN, Marius Leonard OLAR, Monica LEBA, Andreea IONICA, Jose MACHADO

Abstract


This study focuses on improving biomechanical analysis and rehabilitation methods using predictive modeling. It aims to create a reliable predictive model using Support Vector Regression (SVR) to predict arm movements based on torso orientation. By processing data from Inertial Measurement Units attached to participants' torsos and arms, the study explores the potential of SVR in physical therapy. The model's accuracy is evaluated using statistical metrics like Mean Absolute Error and coefficient of determination R2, demonstrating its effectiveness in predicting human movements. This work supports advancements in rehabilitation, setting a precedent for future research into adaptive, high-accuracy predictive modeling in health-related applications.


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References


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