# Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior

arXiv: https://arxiv.org/abs/2006.09267 · HTML: https://mlsanity.com/paper/2006.09267/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Situation Awareness for Driver-Centric Driving Style Adaptation](https://mlsanity.com/paper/2403.19595/) (2024)
- [Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition](https://mlsanity.com/paper/2409.16730/) (2024)

## Abstract (arXiv)

Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases, compared to when the drivers are classified with no generated data.