Teaching motor skills such as playing music, handwriting, and driving, can
greatly benefit from recently developed technologies such as wearable gloves
for haptic feedback or robotic sensorimotor exoskeletons for the mediation of
effective human-human and robot-human physical interactions. At the heart of
such teacher-learner interactions still stands the critical role of the ongoing
feedback a teacher can get about the student's engagement state during the
learning and practice sessions. Particularly for motor learning, such feedback
is an essential functionality in a system that is developed to guide a teacher
on how to control the intensity of the physical interaction, and to best adapt
it to the gradually evolving performance of the learner. In this paper, our
focus is on the development of a near real-time machine-learning model that can
acquire its input from a set of readily available, noninvasive,
privacy-preserving, body-worn sensors, for the benefit of tracking the
engagement of the learner in the motor task. We used the specific case of
violin playing as a target domain in which data were empirically acquired, the
latent construct of engagement in motor learning was carefully developed for
data labeling, and a machine-learning model was rigorously trained and
validated