Movement in Video Classification using Structured Data: Workout Videos Application

Jonathan Múnera* and Marta Silvia Tabares
Grupo GIDITIC/Universidad EAFIT, Medellín, Colombia
*Corresponding Author: Jonathan Múnera, Grupo GIDITIC/Universidad EAFIT, Medellín, Colombia.

Published: December 04, 2023

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Abstract  

Nowadays, several video movement classification methodologies are based on reading and processing each frame using image classification algorithms. However, it is rare to find approaches using angle distribution over time. This paper proposes video movement classification based on the exercise states calculated from each frame’s angles. Different video classification approaches and their respective variables and models were analyzed to achieve this, using unstructured data: images. Besides, structure data as angles from critical joints Armpits, legs, elbows, hips, and torso inclination were calculated directly from workout videos, allowing the implementation of classification models such as the KNN and Decision Trees. The result shows these techniques can achieve similar accuracy, close to 95%, concerning Neural Networks algorithms, the primary model used in the previously mentioned approaches. Finally, it was possible to conclude that using structured data for movement classification models allows for lower performance costs and computing resources than using unstructured data without compromising the quality of the model.

Keywords: Machine Learning; computer vision; KNN; mediapipe; supervised classification; video classification; deep learning; tensorflow; neural network; workout; fitness; exercise; signal

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