Improve Exercise Movement: Detecting Mistakes on Yoga with Mediapipe and MLP

Abstract

Yoga is known as a comprehensive practice for maintaining physical and mental health. However, improper execution of yoga postures can cause injury, hinder progress, and potentially damage health. To overcome this problem, this research utilizes Mediapipe as a data preprocessing tool to identify yoga poses, which are then classified using the Multi-Layer Perceptron (MLP) algorithm. In the process, data normalization is carried out to increase prediction accuracy. This research uses a dataset consisting of six classes of yoga poses, namely tree, downdog, goddess, warrior, and plank. Experimental results show that the model achieved 98% accuracy during training, but accuracy during testing decreased to 95%. This shows an indication of overfitting, where the model adapts too much to the training data and is less able to generalize to the test data. This study makes an important contribution to the development of a safer and more accurate yoga pose classification system, which can be applied to practice yoga properly and prevent injuries.