[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124381-en":3,"doc-seo-124381-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124381,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Real-time machine learning-based posture correction for enhanced exercise performance - Abstract","Real-time machine learning-based posture correction system targets improved exercise performance and reduced injury risk by delivering instant feedback during squats and planks. The approach combines MediaPipe pose estimation with OpenCV video-frame analysis to detect poor posture and evaluate joint-angle relationships across arms, knees, back, and hips. A custom real-world workout dataset trains posture reliability across varied user conditions. The goal is accessible, scalable form assessment without expert supervision, supporting better training habits.","Real-time machine learning-based posture correction for enhanced exercise performance  \nAnish Khadtare, Vasistha Ved, Himanshu Kotak, Akhil Jain, Pinki Vishwakarma  \nDepartment of Computer Engineering, Shah and Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India  \nArticle history:  \nReceived Jul 18, 2024 Revised Apr 12, 2025 Accepted May 24, 2025  \nKeywords:  \nHuman pose estimation  \nMachine learning MediaPipe OpenCV Tensorflow  \nCorresponding Author:  \nPoor posture and associated physical health problems have grown more common as technology use increases, especially during workout sessions. Maintaining proper posture is essential to increasing the efficacy of your workouts and avoiding injuries. The research paper presents the development of a machine-learning model designed to provide real-time posture correction and feedback for exercises such as squats and planks. The model uses MediaPipe for precise real-time posture estimation and OpenCV for analyzing video frames. It detects poor posture and provides users with instant corrective feedback on their posture by examining the angles between important body parts, such as the arms, knees, back, and hips. This innovative method enables a thorough evaluation of form without requiring face-to-face supervision, opening it up to a wider audience. The model is trained on real-world workout datasets of people performing exercises indifferent positions and postures to ensure that posture detection is reliable under various user circumstances. The system utilizes cutting-edge machinelearning algorithms to demonstrate scalability and adaptability for future training types beyond squats and planks. The main goal is to provide users with a model that increases the efficacy of workouts, lowers the risk of injury, and encourages better exercise habits. The model's emphasis on usability and accessibility makes it potentially a vital tool for anyone looking to enhance their posture and general fitness levels.  \nThis is an open access article under the CC BY-SA license.  \nAnish Khadtare  \nDepartment of Computer Engineering, Shah and Anchor Kutchhi Engineering College Mumbai-400088, Maharashtra, India  \nEmail: [anish.khadtare15636@sakec.ac.in](anish.khadtare15636@sakec.ac.in)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMaintaining a proper posture when exercising is crucial for maximizing physical performance and lowering the chance of injury [1] . Regular physical activity is beneficial for cardiovascular health, muscular strength, and general well-being, according to several studies. According to the World Health Organization, nearly 31% of the world's adult population, amounting to over 1.8 billion adults, are physically inactive [2] . However, especially for those who are new to exercising, poor exercise practices can result in a variety of difficulties, such as acute injuries and long-term musculoskeletal concerns. This emphasizes how crucial it is to maintain proper form, particularly while performing basic exercises like planks and squats.  \nTechnological developments have made it easier to develop technology for tracking and adjusting posture while exercising. Recent studies have proposed machine learning and computer vision techniques such as convolutional neural networks (CNN), support vector machines (SVM), MediaPipe pose, and DeepPose are used to develop models for analyzing and examining human body posture. Many of the current  \nsolutions, however, need modern technology or skilled personnel, which may restrict accessibility for people in Tier 2 and Tier 3 cities where these resources might not be easily accessible [3]–[5] .  \nEven with great advancements, there are still difficulties in developing real-time feedback systems that let users examine their body posture during workouts on their own without expert supervision. For those who want to get fitter in distant or disadvantaged locations, traditional training techniques sometimes need in-person instruction, which ca","cbCaisRMfwyu11oG","https://ap.wps.com/l/cbCaisRMfwyu11oG","pdf",477901,1,8,"English","en",105,"# 1. INTRODUCTION\n## Physical inactivity and injury risk\n## Need for accessible posture feedback\n# 2. RESEARCH METHOD\n## 2.1 Related work","[{\"question\":\"What exercises does the system focus on for posture correction?\",\"answer\":\"The model is designed for exercises such as squats and planks, detecting poor posture and providing corrective feedback in real time.\"},{\"question\":\"How does the system estimate posture and determine whether form is correct?\",\"answer\":\"It uses MediaPipe for real-time pose estimation and OpenCV to analyze video frames, then evaluates angles between key body joints like arms, knees, back, and hips.\"},{\"question\":\"Why is the proposed approach intended to be more accessible than traditional training?\",\"answer\":\"It aims to reduce reliance on skilled personnel and expensive in-person coaching by enabling users to check exercise form without face-to-face supervision, particularly in resource-limited locations.\"}]","Real-time machine learning-based posture correction for enhanced exercise performance - Abstract | 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