7 research outputs found
Ongoing Tracking of Engagement in Motor Learning
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
Understanding the Properties of Generated Corpora
Models for text generation have become focal for many research tasks and
especially for the generation of sentence corpora. However, understanding the
properties of an automatically generated text corpus remains challenging. We
propose a set of tools that examine the properties of generated text corpora.
Applying these tools on various generated corpora allowed us to gain new
insights into the properties of the generative models. As part of our
characterization process, we found remarkable differences in the corpora
generated by two leading generative technologies
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition
Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such teaching processes. In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition. We introduce an AI teacher model that captures the distinct characteristics of human instructors. Using a reinforcement learning environment tailored to mimic teacher-learner interactions, we tested our AI model against four guiding hypotheses, emphasizing improved learner performance, enhanced rate of skill acquisition, and reduced variability in learning outcomes. Our findings, validated on synthetic learners, revealed significant improvements across all tested hypotheses. Notably, our model showcased robustness across different learners and settings and demonstrated adaptability to handwriting. This research underscores the potential of integrating Imitation and Reinforcement Learning models with robotics in revolutionizing the teaching of critical motor skills