WiseMotion

Inspiration

Inspiration: WiseMotion finds its inspiration in a sobering reality: according to the National Safety Council, drowsy driving accounts for 100,000 crashes, resulting in 1,550 fatalities annually. These alarming statistics emphasize the severity of the problem and the often underestimated consequences of maintaining poor posture while driving. Each year, countless lives are lost, and injuries are sustained due to these issues. It became evident that there was a pressing need to address this issue comprehensively and proactively. That is why our goal was to harness technology to create a solution that could save lives and promote healthier habits on the road.

What it does

WiseMotion is a groundbreaking solution that combines eye tracking and motion detection technologies to enhance driving safety. It monitors a driver's alertness levels, gauging for signs of drowsiness, and assesses their posture to ensure a safe and comfortable driving experience. When your face show signs of fatigue or your posture deviates from the ideal, WiseMotion provides real-time alerts, helping you stay vigilant and maintain proper positioning, reducing the risk of accidents.

How we built it

WiseMotion seamlessly integrates two critical aspects of driving safety using a local webcam. Firstly, it evaluates a driver's posture by identifying key body points and calculating angles based on the RULA model, offering reminders for ergonomic sitting. Secondly, it assesses facial features and eyes to detect drowsiness, issuing verbal prompts when needed. It employs Haar Cascade classifiers, pre-trained machine learning models from Dlib, and Eye Aspect Ratio (EAR) calculations for precise drowsiness detection, promoting proper posture and alertness for a safer driving experience.

Challenges we ran into

Our journey with WiseMotion presented its share of challenges. Initially, we had planned to use the AdHawk glasses for eye tracking and pupil-size analysis, but we encountered difficulties extracting the pupil data needed to train a model. This setback prompted us to pivot and switch to Python's OpenCV and pre-trained machine learning models, which required a quick adaptation to new tools and methods. The transition was challenging, but it ultimately strengthened our problem-solving skills and resilience, leading us to successfully complete the project.

Accomplishments that we're proud of

We take pride in achieving our goal of delivering a functional product that utilizes cutting-edge APIs to address a real-world problem. WiseMotion showcases our commitment to improving safety on the road and promoting better driving habits. Learning how to integrate these technologies and overcome obstacles within a tight time frame was a significant achievement for our team. We're excited to see WiseMotion make a positive impact on the lives of drivers.

What we learned

Our journey with WiseMotion taught us invaluable lessons. We realized that the initial ideas, even if they face challenges, lay the foundation for greater innovations. We learned the importance of adaptability and perseverance when encountering setbacks. Most importantly, we honed our ability to work effectively under significant time constraints, completing our project within a 24-hour timeframe. These lessons will undoubtedly guide us in future endeavors.

What's next for WiseMotion

The possibilities for WiseMotion are vast. Beyond road safety, we envision its implementation in various environments, such as schools. By tracking students' eye movements while taking tests and assessing their posture continuously, WiseMotion can provide insights to educators, enhancing the learning environment. We are committed to further developing and refining WiseMotion's capabilities to continue making a positive impact on safety and well-being.