Machine Learning-Based Detection of 2 Riders of E-Scooters

Abstract

The thesis is about detecting the number of people on an e-scooter using supervised machine learning. The purpose of this project is to train a model to distinguish between single-driver and double-driver on an e-scooter, this should be done through a series of measurements with different people, people with experience, and not, those of different sizes and heights. After having some graphs using an accelerometer, the gathered data is used to train the model, and then it will be trained to differentiate between the number of people in an e-scooter.