Ongoing Tracking of Engagement in Motor Learning

TL;DR

This paper presents a real-time machine learning approach to monitor learner engagement during motor skill training, using noninvasive wearable sensors with a focus on violin playing.

Contribution

It introduces a novel, privacy-preserving sensor-based system for tracking engagement in motor learning, specifically validated in violin practice.

Findings

Successfully developed a machine learning model for engagement detection

Validated the model with empirical data from violin players

Demonstrated near real-time tracking capabilities

Abstract

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.