Pilates Pose Classification Using MediaPipe and Convolutional Neural Networks with Transfer Learning

Tanjaya, Kenneth Angelo and Naufal, Mohammad Farid and Arwoko, Heru Pilates Pose Classification Using MediaPipe and Convolutional Neural Networks with Transfer Learning. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 9 (2). pp. 212-222. ISSN 2338-3070

Text 1-Pilates Pose 1-Classification using MediaPipe and Convolutional Neural Networks with Transfer Learning.pdf Download (848kB)

Abstract

A sedentary lifestyle can lead to heart disease, cancer, and type 2 diabetes. An anaerobic exercise called pilates can address these problems. Although pilates training can provide health benefits, the heavy load of pilates poses may cause severe muscle injury if not done properly. Surveys have found that many teenagers are unaware of the movements in pilates poses. Therefore, a system is needed to help users classify pilates poses accurately. MediaPipe is a system that accurately extracts the real time human body skeleton. Convolutional Neural Network (CNN) with transfer learning is an accurate method for image classification. There have been several studies investigated pilates poses classification. However, there is still no research applies the MediaPipe as a skeleton feature extractor and CNN with a transfer learning to classify pilates poses. In addition, previous research still does not implement the pilates poses classification in real-time. Based on this problem, this study creates a system using MediaPipe as a feature extractor and CNN with transfer learning as a real-time pilates poses classifier. This study utilized five types of Pilates poses: Warrior, Tree, Plank, Goddess, and Downward Dog.This system runs on a mobile device and gets information from a camera sensor. The results from MediaPipe then be classified by pre-trained CNN architectures with transfer learning: MobileNetV2, Xception, and ResNet50. The best model was obtained by MobileNetV2, which had an f1 score of 98%. Ten people who didn't know much about Pilates also tested the system. They all agreed that the app could accurately identify Pilates poses, make people more interested in Pilates, and help them learn more about Pilates.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	02 May 2023 01:59
Last Modified:	02 May 2023 01:59
URI:	http://eprints.uad.ac.id/id/eprint/43074

Actions (login required)

View Item