Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing

Xuelian Cheng; Mingyi He; Weijun Duan

doi:10.1109/ICOT.2017.8336075

Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing

Xuelian Cheng, Mingyi He, Weijun Duan

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

5 Citations (Scopus)

Abstract

A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.

Original language	English
Title of host publication	Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Editors	Minghui Dong, Lei Wang, Yanfeng Lu, Haizhou Li
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	7-10
Number of pages	4
ISBN (Electronic)	9781538632758
DOIs	https://doi.org/10.1109/ICOT.2017.8336075
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Orange Technologies 2017 - Singapore, Singapore Duration: 8 Dec 2017 → 10 Dec 2017 Conference number: 5th https://ieeexplore.ieee.org/xpl/conhome/8331062/proceeding (Proceedings)

Conference

Conference	International Conference on Orange Technologies 2017
Abbreviated title	ICOT 2017
Country/Territory	Singapore
City	Singapore
Period	8/12/17 → 10/12/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8331062/proceeding (Proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Human posture recognition
Kinect sensor
Physical fitness
Skeletal smoothing

Access to Document

10.1109/ICOT.2017.8336075

Cite this

Cheng, X., He, M., & Duan, W. (2017). Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing. In M. Dong, L. Wang, Y. Lu, & H. Li (Eds.), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (pp. 7-10). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICOT.2017.8336075

Cheng, Xuelian ; He, Mingyi ; Duan, Weijun. / Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. editor / Minghui Dong ; Lei Wang ; Yanfeng Lu ; Haizhou Li. IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 7-10

@inproceedings{09d347d53ece450b966f3cdc520da063,

title = "Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing",

abstract = "A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.",

keywords = "Human posture recognition, Kinect sensor, Physical fitness, Skeletal smoothing",

author = "Xuelian Cheng and Mingyi He and Weijun Duan",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; International Conference on Orange Technologies 2017, ICOT 2017 ; Conference date: 08-12-2017 Through 10-12-2017",

year = "2017",

doi = "10.1109/ICOT.2017.8336075",

language = "English",

pages = "7--10",

editor = "Minghui Dong and Lei Wang and Yanfeng Lu and Haizhou Li",

booktitle = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/8331062/proceeding",

}

Cheng, X, He, M & Duan, W 2017, Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing. in M Dong, L Wang, Y Lu & H Li (eds), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. IEEE, Institute of Electrical and Electronics Engineers, pp. 7-10, International Conference on Orange Technologies 2017, Singapore, Singapore, 8/12/17. https://doi.org/10.1109/ICOT.2017.8336075

Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing. / Cheng, Xuelian; He, Mingyi; Duan, Weijun.
Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. ed. / Minghui Dong; Lei Wang; Yanfeng Lu; Haizhou Li. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 7-10.

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

TY - GEN

T1 - Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing

AU - Cheng, Xuelian

AU - He, Mingyi

AU - Duan, Weijun

N1 - Conference code: 5th

PY - 2017

Y1 - 2017

N2 - A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.

AB - A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.

KW - Human posture recognition

KW - Kinect sensor

KW - Physical fitness

KW - Skeletal smoothing

UR - https://www.scopus.com/pages/publications/85048545772

U2 - 10.1109/ICOT.2017.8336075

DO - 10.1109/ICOT.2017.8336075

M3 - Conference Paper

AN - SCOPUS:85048545772

SP - 7

EP - 10

BT - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017

A2 - Dong, Minghui

A2 - Wang, Lei

A2 - Lu, Yanfeng

A2 - Li, Haizhou

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - International Conference on Orange Technologies 2017

Y2 - 8 December 2017 through 10 December 2017

ER -

Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing

Abstract

Conference

UN SDGs

Keywords

Access to Document

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