Safar: Real-Time Accident Detection and Emergency
Our system utilizes advanced sensors, such as gyroscopes and accelerometers, in conjunction with mobile and wearable devices, to monitor the rider's posture, heart rate, and speed.
Created on 5th November 2023
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Safar: Real-Time Accident Detection and Emergency
Our system utilizes advanced sensors, such as gyroscopes and accelerometers, in conjunction with mobile and wearable devices, to monitor the rider's posture, heart rate, and speed.
The problem Safar: Real-Time Accident Detection and Emergency solves
The solution addresses the critical issue of bike and vehicle accidents in India, which result in over 400 deaths per day and have a significant economic impact. This system is designed to improve bike safety in India by utilizing advanced sensors and technology to tackle the problem. Here's how it addresses the issue:
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Real-time Accident Detection: The system continuously monitors a rider's posture, heart rate, and speed using gyroscopes and accelerometers. By doing so, it can swiftly detect accidents as they happen, which is crucial for prompt intervention.
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Instant Alerts: In the event of an accident, the system immediately sends alerts to the rider's designated emergency contacts. This rapid communication ensures that help can reach the rider quickly, potentially saving lives.
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Prompt Assistance: By providing timely information to emergency contacts, the system ensures that injured riders receive the necessary medical attention without unnecessary delays, further improving the chances of survival and reducing the severity of injuries.
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Economic Impact: High rates of bike accidents not only lead to loss of life but also result in significant economic costs due to healthcare expenses, lost productivity, and more. This solution can help reduce the economic burden by preventing accidents and improving response times.
How It Makes Existing Tasks Easier and Safer:
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Enhanced Rider Safety: This system fundamentally enhances bike safety in India. Riders can now feel more secure and confident in their journeys, knowing that they have real-time safety monitoring and support.
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Reduced Response Time: In the unfortunate event of an accident, the system significantly reduces the response time, ensuring that emergency services and contacts are informed promptly. This not only saves lives but also minimizes the severity of injuries.
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Peace of Mind: Riders and their families have peace of mind knowing that the technology is actively watching
Challenges we ran into
One of the pivotal challenges we encountered in developing our bike safety system was the complex task of collecting and selecting the right data points for our machine learning (ML) model to accurately detect falls and accidents. This required extensive data collection from real-world scenarios and controlled accident simulations to ensure data comprehensiveness. Additionally, selecting the appropriate parameters for the ML model, involving factors like accelerometer and gyroscope readings, heart rate, and speed, posed another challenge. We overcame these challenges through rigorous data collection protocols, meticulous data labeling, and iterative parameter optimization. Furthermore, due to time constraints, we had to defer the integration of our project with Orkes, prioritizing the core safety features, while keeping the integration as a future development avenue. These experiences underscore the complexity of creating a robust safety solution and emphasize the importance of continuous improvement in the field of safety technology.