Enhancing Human Lifestyle using Artificial Intelligence and Machine Learning

Author Reg. ID: TIJER_151732

Published Paper Id: TIJER2403124

Published In: Volume 11 Issue 3, March-2024

Abstract: In today’s fast-paced world, people find themselves managing multiple responsibilities, leading to increasingly dormant lifestyles and erratic eating habits. Despite awareness of the importance of regular exercise, many struggle to find time amidst their busy schedules, resulting in a growing need for accessible and personalized fitness solutions to promote overall well-being. The proposed work presents a comprehensive approach to personalized health and wellness, comprising a diet recommendation system and a yoga pose recognition system. The diet recommendation system employs a two-step process involving K-Means clustering and Random Forest prediction to generate personalized diet plans based on individual characteristics such as height, weight, activity level, and weight goal. Leveraging advanced machine learning techniques, the system provides tailored dietary guidance to optimize health outcomes. In addition, the yoga pose recognition system utilizes pose detection technology and semantic analysis to accurately identify and evaluate yoga poses. Through the integration of the MoveNet model and TensorFlow.js, the system offers real-time feedback and guidance, facilitating effective yoga practice. Together, the system provides a complete approach to health and well-being, helping users make smart food decisions and participate in safe, beneficial workouts.

Keywords: AI and ML, CNN, MoveNet, OpenCV, Diet recommendation, Pose Estimation, K-means and Random forest

Downloads: 00030

Page No: a925-a932

Country: mumbai, MAHARASHTRA, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403124

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403124

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