Version 1
: Received: 6 June 2023 / Approved: 6 June 2023 / Online: 6 June 2023 (07:26:29 CEST)
How to cite:
Tharatipyakul, A.; Pongnumkul, S. Deep Learning-based Pose Estimation in Providing Feedback for Physical Movement: a Review. Preprints2023, 2023060395. https://doi.org/10.20944/preprints202306.0395.v1
Tharatipyakul, A.; Pongnumkul, S. Deep Learning-based Pose Estimation in Providing Feedback for Physical Movement: a Review. Preprints 2023, 2023060395. https://doi.org/10.20944/preprints202306.0395.v1
Tharatipyakul, A.; Pongnumkul, S. Deep Learning-based Pose Estimation in Providing Feedback for Physical Movement: a Review. Preprints2023, 2023060395. https://doi.org/10.20944/preprints202306.0395.v1
APA Style
Tharatipyakul, A., & Pongnumkul, S. (2023). Deep Learning-based Pose Estimation in Providing Feedback for Physical Movement: a Review. Preprints. https://doi.org/10.20944/preprints202306.0395.v1
Chicago/Turabian Style
Tharatipyakul, A. and Suporn Pongnumkul. 2023 "Deep Learning-based Pose Estimation in Providing Feedback for Physical Movement: a Review" Preprints. https://doi.org/10.20944/preprints202306.0395.v1
Abstract
Pose estimation has various applications in analyzing human movement and behavior, including providing feedback to users about their movements so they could adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human `body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 20 articles for methods used to estimate human pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that while there was an improvement, the majority of pose estimation challenges remain. The effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.
Keywords
Pose estimation; Movement assessment; Augmented feedback; Physical movement; Review
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.