Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

Ho Jun Park; Jang Woon Baek; Jong Hwan Kim

doi:10.1109/CASE48305.2020.9216833

Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

Ho Jun Park
, Jang Woon Baek
, Jong Hwan Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Citations (Scopus)

Abstract

This paper presents a real-time approach to count push-ups using 2D video imagery. The proposed method uses OpenPose in each frame to extract multiple joints and links of a human body. Then, it analyzes key motion features linked to counting the push-ups. Taking in consideration the push-up rules of the Republic of Korea Army, five criteria are defined and used parametrically to discriminate both correct and incorrect push-ups. A total of 147,840 samples have been collected from 220 push-up videos each in two different viewpoints: half of the videos for modeling the proposed method and the other half for testing its performance. Finally, the results shows 90.00%, 87.82%, 97.86%, and 92.57% for accuracy, precision, recall, and F-measure, respectively, demonstrating its reliability in military physical tests.

Original language	English
Title of host publication	2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
Publisher	IEEE Computer Society
Pages	1389-1394
Number of pages	6
ISBN (Electronic)	9781728169040
DOIs	https://doi.org/10.1109/CASE48305.2020.9216833
Publication status	Published - Aug 2020
Event	16th IEEE International Conference on Automation Science and Engineering, CASE 2020 - Hong Kong, Hong Kong Duration: 20 Aug 2020 → 21 Aug 2020

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2020-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Country/Territory	Hong Kong
City	Hong Kong
Period	20/08/20 → 21/08/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Army Physical Fitness Test
OpenPose
Push-up counter
Vision-based detection

Access to Document

10.1109/CASE48305.2020.9216833

Cite this

Park, H. J., Baek, J. W., & Kim, J. H. (2020). Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose. In 2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020 (pp. 1389-1394). Article 9216833 (IEEE International Conference on Automation Science and Engineering; Vol. 2020-August). IEEE Computer Society. https://doi.org/10.1109/CASE48305.2020.9216833

@inproceedings{79d05082c783495b983edea643ecb43c,

title = "Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose",

abstract = "This paper presents a real-time approach to count push-ups using 2D video imagery. The proposed method uses OpenPose in each frame to extract multiple joints and links of a human body. Then, it analyzes key motion features linked to counting the push-ups. Taking in consideration the push-up rules of the Republic of Korea Army, five criteria are defined and used parametrically to discriminate both correct and incorrect push-ups. A total of 147,840 samples have been collected from 220 push-up videos each in two different viewpoints: half of the videos for modeling the proposed method and the other half for testing its performance. Finally, the results shows 90.00\%, 87.82\%, 97.86\%, and 92.57\% for accuracy, precision, recall, and F-measure, respectively, demonstrating its reliability in military physical tests.",

keywords = "Army Physical Fitness Test, OpenPose, Push-up counter, Vision-based detection",

author = "Park, \{Ho Jun\} and Baek, \{Jang Woon\} and Kim, \{Jong Hwan\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 16th IEEE International Conference on Automation Science and Engineering, CASE 2020 ; Conference date: 20-08-2020 Through 21-08-2020",

year = "2020",

month = aug,

doi = "10.1109/CASE48305.2020.9216833",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

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Park, HJ, Baek, JW & Kim, JH 2020, Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose. in 2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020., 9216833, IEEE International Conference on Automation Science and Engineering, vol. 2020-August, IEEE Computer Society, pp. 1389-1394, 16th IEEE International Conference on Automation Science and Engineering, CASE 2020, Hong Kong, Hong Kong, 20/08/20. https://doi.org/10.1109/CASE48305.2020.9216833

Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose. / Park, Ho Jun; Baek, Jang Woon; Kim, Jong Hwan.
2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020. IEEE Computer Society, 2020. p. 1389-1394 9216833 (IEEE International Conference on Automation Science and Engineering; Vol. 2020-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Baek, Jang Woon

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Y1 - 2020/8

N2 - This paper presents a real-time approach to count push-ups using 2D video imagery. The proposed method uses OpenPose in each frame to extract multiple joints and links of a human body. Then, it analyzes key motion features linked to counting the push-ups. Taking in consideration the push-up rules of the Republic of Korea Army, five criteria are defined and used parametrically to discriminate both correct and incorrect push-ups. A total of 147,840 samples have been collected from 220 push-up videos each in two different viewpoints: half of the videos for modeling the proposed method and the other half for testing its performance. Finally, the results shows 90.00%, 87.82%, 97.86%, and 92.57% for accuracy, precision, recall, and F-measure, respectively, demonstrating its reliability in military physical tests.

AB - This paper presents a real-time approach to count push-ups using 2D video imagery. The proposed method uses OpenPose in each frame to extract multiple joints and links of a human body. Then, it analyzes key motion features linked to counting the push-ups. Taking in consideration the push-up rules of the Republic of Korea Army, five criteria are defined and used parametrically to discriminate both correct and incorrect push-ups. A total of 147,840 samples have been collected from 220 push-up videos each in two different viewpoints: half of the videos for modeling the proposed method and the other half for testing its performance. Finally, the results shows 90.00%, 87.82%, 97.86%, and 92.57% for accuracy, precision, recall, and F-measure, respectively, demonstrating its reliability in military physical tests.

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Park HJ, Baek JW, Kim JH. Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose. In 2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020. IEEE Computer Society. 2020. p. 1389-1394. 9216833. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/CASE48305.2020.9216833

Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

Abstract

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Keywords

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