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Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick-hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=-0.973,&amp;nbsp;95%&amp;nbsp;CI&amp;nbsp;-0.981,-0.962,&amp;nbsp;&amp;nbsp;p&lt;0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.

Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study