Tracking hand movements and detecting grasp

Chegani, Rana Sadeghi; Menon, Carlo

doi:10.1109/ccece.2016.7726837

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…In the realm of hand gesture recognition, prior works, as cited in [5] and [6], have explored a system employing the Viola-Jones method, specifically tailored for applications centered around human-computer interaction. One common strategy in hand gesture recognition involves feature extraction from recognized objects, such as the hand, utilizing these features as inputs for a classifier.…”

Section: Literature Surveymentioning

confidence: 99%

“…One common strategy in hand gesture recognition involves feature extraction from recognized objects, such as the hand, utilizing these features as inputs for a classifier. As elucidated in earlier references [5] and [6], Hu employed the Support Vector Machine (SVM) classifier to categorize instances of the hand, relying on invariant instance feature vectors. These Huinvariant features were meticulously designed to capture crucial aspects of hand movement.…”

Section: Literature Surveymentioning

confidence: 99%

“…The primary objective of the system is to aid trainee sewing operators in improving their skills through real-time alerts and immediate feedback. This feature sets it apart from methods such as the Viola-Jones method and Kinect-based approaches described in references [5] and [6]. While these methods are accurate, they may not provide real-time feedback, which is crucial for operators to promptly recognize and rectify their ambiguous hand movements.…”

Section: Comparative Analysismentioning

confidence: 99%

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ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

Tharuka Rathnayake,

Rathnayake,

A. Karunasena

et al. 2023

IRJIET

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In the apparel industry, the training of sewing operators plays a pivotal role in ensuring the production of top-quality garments. This research presents a novel approach to improve training methods through the implementation of a real-time hand movement recognition system. This system is designed to identify omissions and incorrect hand actions, providing immediate alerts based on Garment Standard Data (GSD) for prompt corrective actions. Leveraging advanced computer vision techniques and a graph neural network (GNN), the framework achieves an impressive 85.7% accuracy in monitoring and analyzing sewing operators' hand movements. By comparing detected movements with predefined standards, the system identifies deviations and offers instant feedback to operators. Experimental results underscore the system's effectiveness in pinpointing incorrect steps and hand movements, highlighting the potential of GNNs to elevate training in the apparel industry. The developed system significantly enhances sewing operator efficiency and productivity, ultimately leading to the production of higher-quality garments.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 1 more Smart Citation

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

Tharuka Rathnayake,

Rathnayake,

A. Karunasena

et al. 2023

IRJIET

View full text Add to dashboard Cite

show abstract

“…Previous research has been conducted in recent years to detect the movements of humans. Prior investigations predominantly relied on image-processing models [3], but these methods struggled to capture the intricate nuances of the stitching processes and specific movements. The research builds upon the foundational work laid out in previous studies [4] and [5], which underscored the potential of video processing technology to enhance outcomes while reducing time complexity.…”

Section: Literature Surveymentioning

confidence: 99%

An ML-Based Approach for Optimizing the Productivity and Efficiency of the Apparel Industry by Focusing on Trainee Employees

R.A.S.T,

D.N,

Rathnayake

et al. 2023

IRJIET

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In the apparel industry, training is crucial because it brings skilled workers and promotes increased productivity. However, typical manual approaches frequently fail to accelerate the training process, resulting in unsatisfactory results properly. In this paper, the authors describe an innovative strategy to increase the productivity and efficiency of sewing machine operator training processes by using a machine learning-driven web-based application. The proposed application leverages the power of machine learning models to identify and solve crucial areas for improvement. It specifically detects wrong hand movements, incorrect trainee sitting postures, defects in sewed garments, and errors in dexterity tests during the training period of the sewing operators. Notably, the Graphical Neural Network (GNN) model detects erroneous hand movements with an astonishing 85% accuracy. The Convolutional Neural Network (CNN) model excels in detecting incorrect sitting postures, with an impressive 75% accuracy. Furthermore, the CNN model detects garment defects with an accuracy of 95%, while the CNN model detects test result errors in dexterity tests with an astounding 97% test accuracy. By using the proposed web tool for screening, the authors expect to see a significant increase in trainee productivity and efficiency. Lastly, the machine learning-driven web-based application is a great tool for optimizing the garment industry's training process. Future plans include increasing the application's functionality, introducing new features, and investigating its applicability across multiple sectors within garment manufacturing. By adopting this unique approach, the apparel sector can achieve significant gains in training outcomes, resulting in a more skilled and efficient workforce.

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Tracking hand movements and detecting grasp

Cited by 2 publications

References 18 publications

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

An ML-Based Approach for Optimizing the Productivity and Efficiency of the Apparel Industry by Focusing on Trainee Employees

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