PoseRight: An AI Empowered Automatic Posture Estimation and Correction Model

This paper introduces PoseRight, a novel framework that leverages real-time pose estimation and recognition technology to guide users in performing yoga and gym exercises correctly, thereby reducing the risk of injuries. PoseRight empowers users to achieve effective workouts without the need for gym memberships or personal trainers, addressing the growing demand for accessible fitness solutions. Through extensive experimentation with various deep learning models (CNNs, LSTMs, Transformers) and pose estimation techniques (MediaPipe BlazePose, MoveNet), the proposed framework achieves state-of-the-art performance, surpassing the accuracy of current research. PoseRight demonstrates exceptional test accuracies (99.01% for gym exercises, 96.8% for yoga exercises) while maintaining real-time processing speeds in both domains. This research signifies an advancement towards promoting safe and effective exercise guidance, ultimately fostering healthier lifestyles for users.

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Published
2024-11-13
DOI
https://doi.org/10.1109/miucc62295.2024.10783566
Citations
1
Primary Topic
Ergonomics and Musculoskeletal Disorders
Type
article
Field-Weighted Citation Impact
0.64
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article

PoseRight: An AI Empowered Automatic Posture Estimation and Correction Model

Mohamed Yousef, Hanan Hindy, Yomna A. Kawashti, M M Helmy et al.
1 citations
Ergonomics and Musculoskeletal Disorders
0.64
article

PoseRight: An AI Empowered Automatic Posture Estimation and Correction Model

Mohamed Yousef, Hanan Hindy, Yomna A. Kawashti, M M Helmy, M. Shokry, Mina Khalifa Gendy, Mina Lotfy Favez, Kirollos Aziz Galal
article en
1 citations

Abstract

This paper introduces PoseRight, a novel framework that leverages real-time pose estimation and recognition technology to guide users in performing yoga and gym exercises correctly, thereby reducing the risk of injuries. PoseRight empowers users to achieve effective workouts without the need for gym memberships or personal trainers, addressing the growing demand for accessible fitness solutions. Through extensive experimentation with various deep learning models (CNNs, LSTMs, Transformers) and pose estimation techniques (MediaPipe BlazePose, MoveNet), the proposed framework achieves state-of-the-art performance, surpassing the accuracy of current research. PoseRight demonstrates exceptional test accuracies (99.01% for gym exercises, 96.8% for yoga exercises) while maintaining real-time processing speeds in both domains. This research signifies an advancement towards promoting safe and effective exercise guidance, ultimately fostering healthier lifestyles for users.

Ain Shams University (EG)
Gender equality
Openalex Percentile: Top 24%
Ergonomics and Musculoskeletal Disorders
0.64
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PoseRight: An AI Empowered Automatic Posture Estimation and Correction Model — Mohamed Yousef, Hanan Hindy, et al. · (2024) | TGRS Research Map | TGRS