Nutrilife: Empowering health with diet and nutrition app - Tan, Yuki Lok Yee (2024)

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In Malaysia, the lack of food recognition applications focused on local cuisine presents a challenge, as current solutions primarily cater to Western foods. This gap limits users' access to accurate nutritional information for Malaysian dishes, impeding their health goals and meal planning. This project addresses these issues by developing a food recognition app specifically for Malaysian cuisine. Utilizing a Convolutional Neural Network (CNN) model trained in Google Colab with a dataset of local dishes, the app offers precise nutritional insights, including calories and macronutrients, based on user-captured images. Furthermore, the app enables users to save and revisit their food records. A major innovation of this project involves integrating computer vision and machine learning for real-time fitness guidance, specifically focusing on exercise technique. Using MediaPipe and deep learning algorithms, the app track and analyses users' movements during exercises such as squats and bicep curls, providing corrections to improve technique. This integration aims to enhance user experience and effectiveness in fitness training. Overall, this project strives to bridge gaps in food recognition and fitness tracking through advanced technologies, delivering valuable tools for health and wellness.