Analyzing a Comprehensive Hand-Size Database Using Automated Landmark Detection and Measurement Techniques

Cuc Thi Kim  Nguyen; Tieu Thanh  Le; La Thi Ngoc  Anh

doi:10.6180/jase.202511_28(11).0013

Analyzing a Comprehensive Hand-Size Database Using Automated Landmark Detection and Measurement Techniques

Cuc Thi Kim Nguyen¹This email address is being protected from spambots. You need JavaScript enabled to view it., Tieu Thanh Le², and La Thi Ngoc Anh²This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Precision Engineering & Smart measurements Lab, Hanoi University of Science and Technology, Hanoi 100000, Vietnam

²School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam

Received: September 16, 2024
Accepted: December 17, 2024
Publication Date: March 16, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202511_28(11).0013

Data derived from measurements of human hands are widely utilized across various fields, including virtual reality, medical science, fashion, and the development of hand size systems. The precision of hand size determination critically depends on the accurate extraction of measurement landmarks. This study emphasizes the importance of identifying primary dimensions that influence hand size systems and contributes to the development of a comprehensive hand-size database. We propose a method for measuring 24 distinct hand size parameters using 2D techniques. Our approach leverages the MediaPipe tool for accurate and automatic extraction of hand landmarks, regardless of skin color, alongside automatic hand border detection. The system was calibrated using Level 1-gauge blocks, achieving a relative error of 1.15%. Data were collected from over 500 workers, analyzing both right- and left-hand measurements for eight different male hand sizes, achieving a service rate of 70.3% in this study. This research provides a valuable tool for developing a worker hand size system, facilitating the manufacture of safety gloves and hand tools by offering an automated dataset for hand sizes.

Keywords: Hand measurement; Media Pipe hand; Binary classification; Hand sizes system

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