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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Gym-Buddy |
| Authors Name: | Kritika Pandey , Snehal Kumbhar , Rhea Malpekar , Prof.Poonam Vengurlekar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_215717
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| Published Paper Id: | IJNRD2403308 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Over the past few years, there has been a notable increase in interest within the fitness industry, as more people are actively participating in a variety of exercises and sports. The provision of precise and immediate feedback on exercise form is paramount for optimizing performance, reducing the risk of injuries, and attaining fitness objectives. The ”Gym-Buddy” initiative represents a machine learning endeavor aimed at transforming the fitness landscape by delivering comprehensive pose detection capabilities for exercise and sports enthusiasts. Gym-Buddy utilizes cutting-edge computer vision and deep learning methodologies to analyze and evaluate users’ body positions during different exercise regimens and sports engagements. The key objectives of this project en- compass: Pose Detection: Gym-Buddy employs advanced algorithms for pose estimation to identify and monitor crucial body joints and movements in real-time. This functionality enables the system to furnish accurate feedback re- garding exercise form, posture, and movement precision. Exercise Guidance: The Gym-Buddy system is crafted to provide interactive exercise guidance, assisting users in aligning their bodies correctly, refining their movements, and maintaining proper form throughout their workout sessions. Users can receive visual prompts and auditory feedback via their preferred device, making it a versatile companion for home workouts, gym sessions, and outdoor activities. Gym-Buddy is Accessible via a user-friendly web application, Gym-Buddy is designed to be seamlessly integrated into the routines of individuals at all fitness levels. The machine learning models within the system continuously enhance based on user input, ensuring personalized and precise feedback. The overarching goal of this project is to empower individuals to achieve their fitness and sports performance aspirations by furnishing them with an intelligent and supportive training ally. Gym-Buddy not only enhances workout experiences but also advocates for healthier lifestyles and injury prevention, thereby contributing signif- icantly to the overall well-being of its users. |
| Keywords: | :Machine Learning,Computer Vision,Deep Learning,Pose estimation,Mediapipe blazepose,Convolution neural network. |
| Cite Article: | "Gym-Buddy", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d58-d65, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403308.pdf |
| Downloads: | 00040 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403308 Registration ID: 215717 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: d58-d65 Country: Ambernath (East),Thane, Maharashtra, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403308 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403308 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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