International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Enhanced Fitness Tracking: Physiological Augmentation and Advanced Movement Quantification Using Deep Learning Techniques Karthika Priya D, Kiran Vignesh K, Shruthi M Easwari Engineering College, Ramapuram, Tamil Nadu, India, Easwari Engineering College, Ramapuram Tamil Nadu, India, Easwari Engineering College, Ramapuram Tamil Nadu, India. -----------------------------------------------------------------------***--------------------------------------------------------------------------
Abstract - This innovative project presents a strong methodology to forecast and examine fitness objectives. The model accurately monitors exercises and measures fitness levels by using advanced deep learning and data analysis methods. Traditional exercise monitoring relies on manual input or simple techniques that are not very precise. Our system eliminates the need for manual monitoring by automatically tallying various exercises, regardless of their type, duration, or intensity. The approach is divided into several important stages, beginning with meticulous data preprocessing to address missing values and outliers. By employing the combined potential of Machine Learning and Deep Learning algorithms, the methodology utilizes various techniques, including k-nearest Neighbors, Random Forest, and custom gradient descent. What sets this methodology apart is its incorporation of advanced computer vision techniques, where the detection of Regions of Interest (ROI) is identified, thereby enhancing the analysis of exercise form. The uniqueness of this project lies in its interactive nature, facilitating users to directly compare predictions generated by different algorithms. This allows users to input their exercise data and images for real-time analysis, providing a personalized and dynamic user experience. By adopting this comprehensive approach, users gain valuable insights into their fitness progress and receive practical information to make informed decisions about their fitness routines, ultimately contributing to the promotion of healthier lifestyles.
Keywords — Fitness Goals Tracking, Advanced Deep Learning, Data Analysis, Computer Vision, ROI Detection, Real-time Analysis, Personalized Insights, Dynamic User Experience, Informed Decisions. I.INTRODUCTION In the landscape of fitness tracking and movement quantification, the model pioneers a transformative approach to understanding physical activity. In response to the limitations of conventional tracking methods, this project leverages cutting-edge deep learning technologies to provide users with a more nuanced and personalized perspective on their fitness journeys. Fitness tracking has evolved beyond basic step counting to encompass a holistic view of health and well-being. However, the project recognizes the need for a deeper understanding of human movement beyond generic metrics. By integrating deep learning techniques, the methodology aims to enhance the accuracy and granularity of fitness data, delivering insights that go beyond traditional tracking methods. Advanced Movement Analysis encapsulates the project's core objective. It emphasizes the utilization of deep learning to decode intricate patterns of movement, offering a detailed understanding of various exercises and day-to-day physical activities. This not only improves the precision of fitness data but also empowers users to make more informed decisions about their health and fitness based on a comprehensive analysis of their unique movements. This project represents a paradigm shift in the way we approach fitness tracking, moving away from one-size-fits-all metrics. By embracing deep learning, it offers a more individualized and actionable understanding of physical activity. In a world where personalized well-being is paramount, this project stands at the forefront, guiding individuals toward more informed and tailored fitness experiences.
II.RELATED WORKS Wang and Zheng (2022) explore how smart gadgets and AI services may be integrated into the physical fitness space. Using technologies like Artificial Intelligence (AI) and the Internet of Things (IoT), their research explores how intelligent digital treadmills might transform the fitness equipment market. Although their work demonstrates encouraging progress in fitness detection, it is important to acknowledge that the suggested techniques may not be as scalable due to their dependence on high-quality labelled datasets. Further research and development are necessary to overcome the formidable obstacles posed by the difficulties in adjusting to a variety of weather conditions and real-world situations [1].
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