Three dimensional motion capture applied to violin playing: A study on feasibility and characterization of the motor strategy

Comput Methods Programs Biomed. 2017 Oct;149:19-27. doi: 10.1016/j.cmpb.2017.07.005. Epub 2017 Jul 19.


Background and objective: Playing string instruments requires advanced motor skills and a long training that is often spent in uncomfortable postures that may lead to injuries or musculoskeletal disorders. Thus, it is interesting to objectively characterize the motor strategy adopted by the players. In this work, we implemented a method for the quantitative analysis of the motor performance of a violin player.

Methods: The proposed protocol takes advantage of an optoelectronic system and some infra-red reflecting markers in order to track player's motion. The method was tested on a professional violin player performing a legato bowing task. The biomechanical strategy of the upper limb and bow positioning were described by means of quantitative parameters and motion profiles. Measured quantities were: bow trajectory, angles, tracks, velocity, acceleration and jerk.

Results: A good repeatability of the bowing motion (CV < 2%) and high smoothness (jerk < 5 m/s3) were observed. Motion profiles of shoulder, elbow and wrist were repeatable (CV < 7%) and comparable to the curves observed in other studies. Jerk and acceleration profiles demonstrated high smoothness in the ascending and descending phases of bowing. High variability was instead observed for the neck angle (CV ∼56%).

Conclusions: "Quantitative" measurements, instead of "qualitative" observation, can support the diagnosis of motor disorders and the accurate evaluation of musicians' skills. The proposed protocol is a powerful tool for the description of musician's performance, that may be useful to document improvements in playing abilities and to adjust training strategies.

Keywords: Bowing; Functional evaluation; Motion capture; Motor control; Performance evaluation; Violin playing.

MeSH terms

  • Acceleration
  • Elbow
  • Humans
  • Motor Skills*
  • Music*
  • Posture
  • Shoulder
  • Video Recording / methods*
  • Wrist