Jet lag is a major problem for transcontinental travelers. This project aims to address this issue by providing a solution that measures jet lag and offers recommendations to manage it effectively
The problem we are addressing is the significant impact of jet lag on individuals traveling across different time zones. Jet lag can result in fatigue, cognitive impairments, and overall discomfort. Our goal is to develop a system that can accurately measure jet lag and provide actionable insights to mitigate its effects.
To tackle the problem statement, we have developed a Website (comprehensive solution) that combines backend and frontend technologies. We utilize Python for the backend and React for the frontend.
For the backend implementation, we leverage Python and various deep learning models to calculate fatigue levels and cognitive abilities. We employ techniques such as Convolutional Neural Networks (CNN), You Only Look Once (YOLO), and Long Short-Term Memory (LSTM) models to measure fatigue levels accurately. This is achieved by analyzing video recordings provided by the user and processing them through the deep learning models.
The frontend of our solution is built using React, a popular JavaScript library for building user interfaces. We gamify the cognitive ability assessment to measure factors like focus and reaction time. By presenting interactive games and analyzing user performance, we can derive cognitive ability metrics.
Our solution provides a comprehensive measurement of jet lag by considering multiple factors, including fatigue levels and cognitive abilities. The backend deep learning models calculate the fatigue level based on the video recordings, while the frontend gamified assessments determine cognitive abilities such as focus and reaction time.
Based on the measurement results, our system generates a jet lag score that reflects the overall severity of jet lag experienced by the individual. This score takes into account factors like fatigue levels, cognitive abilities, and possibly other relevant parameters. The severity level indicates the intensity of jet lag and helps users understand the impact it has on their well-being.
To assist travelers in managing jet lag, our solution provides personalized recommendations based on the severity level of their jet lag. These recommendations are tailored to the individual's specific needs and may include strategies such as adjusting sleep patterns, exposure to natural light, proper hydration, and exercise.
We would like to express our gratitude to the open-source community and all the contributors who have made their libraries, frameworks, and tools available for use in this project. We also extend our thanks to our mentors and advisors for their guidance and support throughout the development process.