Journal of Computer Assisted Learning Volume 34, Number 1, ISSN 1365-2729 Publisher: Wiley
This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel-throwing pottery. In the case of self-instruction or training, learners face difficulties due to the absence of the teacher/expert and the consequent lack of guidance. Motion capture technologies, machine learning, and gesture recognition may provide a way of overcoming such issues. The proposed methodology is used to record and model expert gestures and then to compare this model in real time with the gestures performed by the learner. Differences in kinematic aspects such as hand distances are detected, and optical/sonic sensorimotor feedback is provided to the learner by the system, alerting him/her when errors occur and guiding him/her to achieve better results. In the case described here, the system was evaluated with 11 learners. With the use of our system, the gestural performance of learners during self-training has been improved in comparison to cases of self-training without computer assistance.
Glushkova, A. & Manitsaris, S. (2018). Gesture recognition and sensorimotor learning-by-doing of motor skills in manual professions: A case study in the wheel-throwing art of pottery. Journal of Computer Assisted Learning, 34(1), 20-31. Wiley. Retrieved February 18, 2019 from https://www.learntechlib.org/p/182202/.