Development Of A Mobile-Based Accident Detection And Notification System With Multi-Modal Data - Alu , Micheal Damilola; Ogunde, Adewale Opeoluwa; Odim, Mba Obasi; Olowookere , Toluwase Ayobami (Corpus Intellectual, 2024)
Road accidents is one of the factors responsible for fatalities and lifelong disabilities globally. The predominant increase in mortality rate on the highway has been attributed to the late arrival of emergency authorities, which could be because of delays in reporting time. The literature has suggested a number of approaches to deal with this significant issue. Some of these approaches include the use of machine learning algorithms. However, many of these works have been limited to single-mode (video, audio, or image) accident detection methods. In addition, some of the existing study explore the use of intelligence transportation system, which could be considered expensive to use. This research has developed a mobile-based accident detection and notification system with multi-modal data. The detection system has incorporated trained simulated data that is validated by unseen data online. The simulated data was characterized by some selected modal data (accelerometer, sound, and gravitational force magnitude) equivalent to the online features. The simulated data was trained using the multilayered perceptron artificial neural network (ANN) model. The trained model was tested using online data from the data world platform. The observation with the simulated data showed that the model achieved an accuracy of 99.5%. The result of the experiment on the online data showed 99.8% accuracy. At the end of the modelling, the ANN model was integrated into an android mobile-based accident detection and notification system. Furthermore, the system was tested with several case study and the result showed that the system performed as expected.