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.

翻译：教授音乐演奏、手写和驾驶等运动技能，可极大受益于近期发展的技术，如用于触觉反馈的可穿戴手套，或用于调节有效的人-人及机器人-人物理交互的机器人感觉运动外骨骼。在此类教-学互动中，关键环节依然是教师在学习与练习过程中所能获得的关于学生投入状态的持续反馈。尤其对于运动学习而言，这种反馈是开发引导教师如何控制物理交互强度并使其最佳适应学习者逐渐演变的表现的系统的核心功能。本文聚焦于开发一种近实时机器学习模型，该模型可从一组现成的、非侵入性、保护隐私的可穿戴传感器获取输入，以跟踪学习者在运动任务中的投入度。我们以小提琴演奏作为目标领域进行实证数据采集，精心构建了运动学习中投入度的潜在构念用于数据标注，并严格训练和验证了机器学习模型。