Temporal distance matrices for squat classification

Ryoji Ogata, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When working out, it is necessary to perform the same action many times for it to have effect. If the action, such as squats or bench pressing, is performed with poor form, it can lead to serious injuries in the long term. For this purpose, we present an action dataset of squats where different types of poor form have been annotated with a diversity of users and backgrounds, and propose a model, based on temporal distance matrices, for the classification task. We first run a 3D pose detector, then we normalize the pose and compute the distance matrix, in which each element represents the distance between two joints. This representation is invariant to differences in individuals, global translation, and global rotation, allowing for high generalization to real world data. Our classification model consists of a CNN with 1D convolutions. Results show that our method significantly outperforms existing approaches for the task.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages2533-2542
Number of pages10
ISBN (Electronic)9781728125060
DOIs
Publication statusPublished - 2019 Jun
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: 2019 Jun 162019 Jun 20

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
CountryUnited States
CityLong Beach
Period19/6/1619/6/20

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Ogata, R., Simo-Serra, E., Iizuka, S., & Ishikawa, H. (2019). Temporal distance matrices for squat classification. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 2533-2542). [9025605] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00309