An Innovative Non-Invasive Approach to Personalized Nutrition and Metabolism Monitoring Using Wearable Sensors

This study introduces an innovative approach to nutrition and body metabolism monitoring using spectroscopy and EMG sensors with cloud-based machine learning. The methodology begins with the setup of spectroscopy and EMG sensors for data collection, followed by robust data preprocessing techniques, including noise filtering, normalization, and imputation. By implementing the machine learning algorithms, encompassing regression and classification models, are evaluated for their suitability in predicting nutrition content and body metabolism. The system implemented from starting with the collection of a diverse dataset and employing feature extraction techniques on spectroscopy and EMG data. The successful prediction of nutrition content and body metabolism parameters is demonstrated, indicating the system's potential to transform how individuals manage their health.The study's tangible outcomes are presented through the display of monitoring parameters, showcasing real-time values of carbohydrates, protein, and fat obtained from spectroscopy sensor data stored in the cloud. The implemented non-invasive method using spectroscopy sensing holds promise for revolutionizing individual health management, offering actionable insights into dietary choices and metabolic health. The presented results contribute to the advancement of technology-driven healthcare solutions, paving the way for improved personalized health monitoring and lifestyle management.