# Surface Recognition for e-Scooter Using Smartphone IMU Sensor

arXiv: https://arxiv.org/abs/2302.12720 · HTML: https://mlsanity.com/paper/2302.12720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [A Hybrid Model and Learning-Based Adaptive Navigation Filter](https://mlsanity.com/paper/2207.12082/) (2022)
- [Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation](https://mlsanity.com/paper/2205.07883/) (2022)
- [Deep Learning for Inertial Sensor Alignment](https://mlsanity.com/paper/2212.11120/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Position From Vehicle Vibration Using an Inertial Measurement Unit](https://mlsanity.com/paper/2303.03942/) (2023)

## Abstract (arXiv)

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.