Determination of anthropometric indicators of the hand of athletes on the basis of computer vision

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PhD, Associate Professor A.A. Pomerantsev1
V.E. Bespyatkin1
D.A. Travkov2
T.V. Bakhtiarova1
1Lipetsk State Pedagogical P. Semenov-Tyan-Shansky University, Lipetsk
2 Lipetsk Regional Clinical Hospital, Lipetsk

In most sports, athletes interact with the outside world through various grips: holding their own weight (hanging on the bar, standing on the uneven bars), holding equipment (racquet, ball, paddle) or interacting with an opponent (martial arts). The linear dimensions of the hand in many sports determine the success of competitive activity. So, weightlifters have a longer hand than the average person, as this contributes to a stronger grip on the bar. In handball, a large brush size contributes to a stronger hold on the ball. Despite the long history of studying the anthropometry of the hand, the classical methods are still the main methods for changing its parameters - measurements using a centimeter tape and a ruler.
Objective of the study was to develop a method for measuring the anthropometric parameters of the hand based on computer vision.
The proposed method is based on the use of a neural network that allows you to automatically determine the coordinates of the nodal points of the palm. The developed computer application with the working title "PalmAnthropometry_1.0" is based on the use of the MediaPipe open source framework, namely the Mediapipe Hands neural network, which allows you to determine the nodal points of the hand by analyzing the video stream. To determine the anthropometric parameters, a hand skeleton is used, which includes 21 nodal points. Using the coordinates of the nodal points and the formulas of analytical geometry on the plane, the linear and angular characteristics of the brush are found.
Results and conclusions. The created application was tested on athletes and showed high speed and accuracy of measurements. The developed method makes it possible to identify eight linear and five angular characteristics of the hand in a few seconds. Based on the results of the study, the data are automatically saved to a protocol file with the .xlsx extension, which allows for mathematical and statistical processing. The proposed method for determining the size of the hand based on computer vision allows you to quickly (several seconds) and accurately find the anthropometric parameters of the hand. In the future, the proposed method can be used in many sports in order to determine the influence of the size of the hand of athletes on the success of competitive activity.

Keywords: anthropometry, hand, neural network, MediaPipe, fine motor skills, computer vision.

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