Real-time Accelerometer-Based Event Detection Through KNN Classification Method

Abstract

Real-time pattern and activity recognition techniques have experienced a recent surge in the use of advanced statistical techniques for analyzing data. More explicitly, Machine Learning classification methods have been used in a variety of applications and for different purposes, such as recognizing different types of events from accelerometer data (e.g. in smart watches, wearables, and vibration analysis). The objective of this work is to present a novel use of accelerometer-based pattern recognition techniques to detect the urban space infrastructure characteristics based on their effect on a vehicle’s body frame dynamics. For this study, we focus on detecting speed bumps, potholes and curves based on a real-time data streaming with the objective of rendering a street digitalization to automatically update an urban space Digital Twin. The present study has achieved real-time event detection for an urban trajectory with high accuracy which yields data that can be later fused with other sensors data. This approach is effectively contributing to the infrastructure layer of a multi-layered approach of digital modelling. Furthermore, the results contribute to expand the body of knowledge of real-time accelerometer event recognition techniques.