RiderRescue: Motorcycle Crash Detection for Emergency Response Using Support Vector Machine Algorithm in MATLAB

Motorcycle accidents are a global issue, often leading to severe injuries or fatalities due to delayed emergency responses. In response, RiderRescue introduces an innovative IoT-based crash detection system designed to enhance motorbike safety. The system utilizes a Support Vector Machine (SVM) algorithm implemented in MATLAB to distinguish between minor and major accidents, reducing false positives and ensuring alerts are triggered only in critical situations. Equipped with an accelerometer, gyroscope, GPS module, and GSM, the system monitors motorcycle movements, detects crashes, and instantly transmits the precise location to emergency contacts via SMS and application notifications. The project methodology encompasses designing system hardware and integrating machine learning algorithms for crash detection. Extensive testing demonstrated RiderRescue's reliability and effectiveness, particularly in areas with poor network connectivity. The SVM algorithm achieved a high accuracy rate, effectively differentiating crash events from non-crash scenarios like sudden braking or parking. This distinction minimizes false alarms, maintaining user trust and system integrity. RiderRescue exemplifies the potential of IoT and machine learning to address transportation safety challenges. Its cost-effectiveness and scalability further position RiderRescue as a practical solution for improving road safety. The study highlights the critical role of integrating advanced sensor technologies and communication systems into emergency response frameworks. It also underscores the need for ongoing calibration and testing across diverse environmental conditions and motorcycle types to ensure consistent performance. RiderRescue offers a promising step forward in motorbike safety, paving the way for smarter, more responsive transportation systems prioritizing rider well-being.