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ExemPoser: Predicting Poses of Experts as Examples for Beginners in Climbing Using a Neural Network

Published: 06 June 2020 Publication History

Abstract

It is important for beginners to imitate poses of experts in various sports; especially in sport climbing, performance depends greatly on the pose that should be taken for given holds. However, it is difficult for beginners to learn the proper poses for all patterns from experts since climbing holds are completely different for each course. Therefore, we propose a system that predict a pose of experts from the positions of the hands and feet of the climber--the positions of holds used by the climber--using a neural network. In other words, our system simulates what pose experts take for the holds the climber is now using. The positions of hands and feet are calculated from a image of the climber captured from behind. To allow users to check what pose is ideal in real time during practice, we have adopted a simple and lightweight network structure with little computational delay. We asked experts to compare the poses predicted by our system with the poses of beginners, and we confirmed that the poses predicted by our system were in most cases better than or as good as those of beginners.

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Cited By

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  • (2024)Towards Automatic Object Detection and Activity Recognition in Indoor ClimbingSensors10.3390/s2419647924:19(6479)Online publication date: 8-Oct-2024
  • (2024)Detection of Lowering in Sport Climbing Using Orientation-Based Sensor-Enhanced Quickdraws: A Preliminary InvestigationSensors10.3390/s2414457624:14(4576)Online publication date: 15-Jul-2024
  • (2024)Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the QuickdrawsBody Area Networks. Smart IoT and Big Data for Intelligent Health Management10.1007/978-3-031-72524-1_14(177-193)Online publication date: 27-Dec-2024
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cover image ACM Other conferences
AHs '20: Proceedings of the Augmented Humans International Conference
March 2020
296 pages
ISBN:9781450376037
DOI:10.1145/3384657
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2020

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Author Tags

  1. climbing
  2. neural networks
  3. sports technologies

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AHs '20
AHs '20: Augmented Humans International Conference
March 16 - 17, 2020
Kaiserslautern, Germany

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Cited By

View all
  • (2024)Towards Automatic Object Detection and Activity Recognition in Indoor ClimbingSensors10.3390/s2419647924:19(6479)Online publication date: 8-Oct-2024
  • (2024)Detection of Lowering in Sport Climbing Using Orientation-Based Sensor-Enhanced Quickdraws: A Preliminary InvestigationSensors10.3390/s2414457624:14(4576)Online publication date: 15-Jul-2024
  • (2024)Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the QuickdrawsBody Area Networks. Smart IoT and Big Data for Intelligent Health Management10.1007/978-3-031-72524-1_14(177-193)Online publication date: 27-Dec-2024
  • (2023)AI Coach: A Motor Skill Training System using Motion Discrepancy DetectionProceedings of the Augmented Humans International Conference 202310.1145/3582700.3582710(179-189)Online publication date: 12-Mar-2023
  • (2023)CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01247(12977-12988)Online publication date: Jun-2023
  • (2022)AI Golf: Golf Swing Analysis Tool for Self-TrainingIEEE Access10.1109/ACCESS.2022.321026110(106286-106295)Online publication date: 2022
  • (2021)Human Augmentation for Skill Acquisition and Skill TransferExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3441354(1-3)Online publication date: 8-May-2021

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