Automated Body Postures Assessment from Still Images Using Mediapipe

Year 2023, Volume: 2 Issue: 2, 240 - 246, 27.12.2023

Mazin H. Aziz Hamed A. Mahmood

Abstract

Human poses assessment was an exciting research trend in the last decades. It was used in sports, health care, and many other fields, to help people get better performance. Machine learning and artificial intelligence techniques are used for this purpose. This paper used Google Mediapipe as a part of a framework for automatic Human-body pose assessment in real time. The proposed framework is based on detecting reference image poses, finding pose landmarks, and extracting discriminative features for each pose. These same process stages are applied to each image frame taken for the trainee using a web camera. The last stage of the framework compares the extracted features for the learner pose image with the saved features of the reference. The comparator specifies the inexact pose for each related human body part frame by frame. The reference image was proposed to enable the system to be used for various applications. Google Mediapipe was used for landmarks detection via Python, which was also used for feature extraction, making comparisons, and giving assessment advice. This system acts like a smart mirror that detects differences between the user pose and the reference still image then gives correction information in real time. Experiments were performed on side view cases like standing and sitting activities and gave promising results. This system could be very helpful for automatically self-pose assessment at home, or as an auxiliary tool for a certain learning program.

Keywords

body postures, pose, landmarks, Mediapipe, Machine Learning

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