Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators

Aastha Arora; Ankit Vijayvargiya; Rajesh Kumar; Manoj Tiwari

doi:10.1109/ICIRCA51532.2021.9544820

Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators

Aastha Arora, Ankit Vijayvargiya, Rajesh Kumar, Manoj Tiwari

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

5 Citations (Scopus)

Abstract

The occurrence of work-related injury risks is extremely high in the garment industry but often ignored. These disorders not only damage the physical health of the workers but also proves to be a prominent factor while talking about loss in work time; ultimately leading to low productivity and efficiency. This paper presents a systematic approach to predict the automated diagnosis of musculoskeletal disorder among the sewing machine operators of the garment industry. The working videos of 20 participants- 10 healthy (normal) and 10 unhealthy (abnormal) were recorded from both sides- left and right. For posture evaluation, OpenPose algorithm is applied to estimate 2D human pose and to extract the joint angles of neck, trunk, upper arm and lower arm of both left and right sides, using the python math library. The extracted angles were then normalised between the range 0 (zero) to 1 (one) to prepare a classification model using the KNN Classifier. Stratified k-fold cross-validation was implemented using 10 folds which gave the accuracy of 91.3% in diagnosing the musculoskeletal disorder among the sewing machine operators.

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1193-1198
Number of pages	6
ISBN (Electronic)	9780738146270
DOIs	https://doi.org/10.1109/ICIRCA51532.2021.9544820
Publication status	Published - 2 Sept 2021
Externally published	Yes
Event	3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 - Coimbatore, India Duration: 2 Sept 2021 → 4 Sept 2021

Publication series

Name	Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

Conference

Conference	3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021
Country/Territory	India
City	Coimbatore
Period	2/09/21 → 4/09/21

Keywords

Convolutional Neural Network
KNN Classifier
Machine Learning
Marker-less Motion Tracking
Musculoskeletal Disorder
OpenPose Algorithm
Pose Estimation

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Safety, Risk, Reliability and Quality
Health Informatics

Access to Document

10.1109/ICIRCA51532.2021.9544820

Cite this

Arora, A., Vijayvargiya, A., Kumar, R., & Tiwari, M. (2021). Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators. In Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 (pp. 1193-1198). (Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIRCA51532.2021.9544820

Arora, Aastha ; Vijayvargiya, Ankit ; Kumar, Rajesh et al. / Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators. Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1193-1198 (Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021).

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title = "Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators",

abstract = "The occurrence of work-related injury risks is extremely high in the garment industry but often ignored. These disorders not only damage the physical health of the workers but also proves to be a prominent factor while talking about loss in work time; ultimately leading to low productivity and efficiency. This paper presents a systematic approach to predict the automated diagnosis of musculoskeletal disorder among the sewing machine operators of the garment industry. The working videos of 20 participants- 10 healthy (normal) and 10 unhealthy (abnormal) were recorded from both sides- left and right. For posture evaluation, OpenPose algorithm is applied to estimate 2D human pose and to extract the joint angles of neck, trunk, upper arm and lower arm of both left and right sides, using the python math library. The extracted angles were then normalised between the range 0 (zero) to 1 (one) to prepare a classification model using the KNN Classifier. Stratified k-fold cross-validation was implemented using 10 folds which gave the accuracy of 91.3% in diagnosing the musculoskeletal disorder among the sewing machine operators.",

keywords = "Convolutional Neural Network, KNN Classifier, Machine Learning, Marker-less Motion Tracking, Musculoskeletal Disorder, OpenPose Algorithm, Pose Estimation",

author = "Aastha Arora and Ankit Vijayvargiya and Rajesh Kumar and Manoj Tiwari",

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Arora, A, Vijayvargiya, A, Kumar, R & Tiwari, M 2021, Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators. in Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021. Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1193-1198, 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, Coimbatore, India, 2/09/21. https://doi.org/10.1109/ICIRCA51532.2021.9544820

Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators. / Arora, Aastha; Vijayvargiya, Ankit; Kumar, Rajesh et al.
Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1193-1198 (Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021).

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

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AU - Vijayvargiya, Ankit

AU - Kumar, Rajesh

AU - Tiwari, Manoj

PY - 2021/9/2

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N2 - The occurrence of work-related injury risks is extremely high in the garment industry but often ignored. These disorders not only damage the physical health of the workers but also proves to be a prominent factor while talking about loss in work time; ultimately leading to low productivity and efficiency. This paper presents a systematic approach to predict the automated diagnosis of musculoskeletal disorder among the sewing machine operators of the garment industry. The working videos of 20 participants- 10 healthy (normal) and 10 unhealthy (abnormal) were recorded from both sides- left and right. For posture evaluation, OpenPose algorithm is applied to estimate 2D human pose and to extract the joint angles of neck, trunk, upper arm and lower arm of both left and right sides, using the python math library. The extracted angles were then normalised between the range 0 (zero) to 1 (one) to prepare a classification model using the KNN Classifier. Stratified k-fold cross-validation was implemented using 10 folds which gave the accuracy of 91.3% in diagnosing the musculoskeletal disorder among the sewing machine operators.

AB - The occurrence of work-related injury risks is extremely high in the garment industry but often ignored. These disorders not only damage the physical health of the workers but also proves to be a prominent factor while talking about loss in work time; ultimately leading to low productivity and efficiency. This paper presents a systematic approach to predict the automated diagnosis of musculoskeletal disorder among the sewing machine operators of the garment industry. The working videos of 20 participants- 10 healthy (normal) and 10 unhealthy (abnormal) were recorded from both sides- left and right. For posture evaluation, OpenPose algorithm is applied to estimate 2D human pose and to extract the joint angles of neck, trunk, upper arm and lower arm of both left and right sides, using the python math library. The extracted angles were then normalised between the range 0 (zero) to 1 (one) to prepare a classification model using the KNN Classifier. Stratified k-fold cross-validation was implemented using 10 folds which gave the accuracy of 91.3% in diagnosing the musculoskeletal disorder among the sewing machine operators.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 2 September 2021 through 4 September 2021

ER -

Arora A, Vijayvargiya A, Kumar R, Tiwari M. Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators. In Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1193-1198. (Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021). doi: 10.1109/ICIRCA51532.2021.9544820

Machine Learning based Risk Classification of Musculoskeletal Disorder among the Garment Industry Operators

Abstract

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Keywords

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