# Surface Recognition for e-Scooter Using Smartphone IMU Sensor

## TL;DR

This paper presents a data-driven deep learning approach using smartphone IMU sensors to accurately distinguish between road and sidewalk surfaces for e-scooter safety enhancement.

## Contribution

It introduces a novel method employing deep neural networks with smartphone IMU data for surface recognition, improving safety and stability in e-scooter operation.

## Key findings

- Deep neural networks outperform classical machine learning models in surface classification.
- The method achieves high accuracy in distinguishing road from sidewalk surfaces.
- Smartphone IMU sensors are effective for real-time surface recognition in e-scooters.

## Abstract

In recent years, as the use of micromobility gained popularity, technological challenges connected to e-scooters became increasingly important. This paper focuses on road surface recognition, an important task in this area. A reliable and accurate method for road surface recognition can help improve the safety and stability of the vehicle. Here a data-driven method is proposed to recognize if an e-scooter is on a road or a sidewalk. The proposed method uses only the widely available inertial measurement unit (IMU) sensors on a smartphone device. deep neural networks (DNNs) are used to infer whether an e-scooteris driving on a road or on a sidewalk by solving a binary classification problem. A data set is collected and several different deep models as well as classical machine learning approaches for the binary classification problem are applied and compared. Experiment results on a route containing the two surfaces are presented demonstrating the DNNs ability to distinguish between them.