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Updated: Jan 21, 2026
08:05
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
11.1K
Dataset on powered two wheelers fall and critical events detection
Abderrahmane Boubezoul1, Fabien Dufour2, Samir Bouaziz3
1University Paris-Est, IFSTTAR, TS2-SIMU&MOTO, 14-20 Boulevard Newton, F-77447 Marne-la-Vallée, France.
Data in Brief
|August 3, 2019
Summary
This study presents data from motorcycle fall experiments using 3D Inertial Measurement Unit sensors. The dataset aids in developing robust motorcycle fall detection algorithms for enhanced safety.
Area of Science:
- Engineering
- Robotics
- Transportation Safety
Background:
- Motorcycle fall detection is challenging due to multifactorial causes.
- Real-world falls are difficult to replicate for data collection.
- Controlled experiments with stunt performers offer a viable alternative.
Purpose of the Study:
- To present a dataset from controlled motorcycle fall experiments.
- To facilitate the development of motorcycle fall detection algorithms.
- To capture data on motorcycle limit handling behavior.
Main Methods:
- Utilized 3D Inertial Measurement Unit (IMU) sensors (accelerometers and gyroscopes).
- Collected data during simulated motorcycle falls under various controlled scenarios.
- Included scenarios like falls in curves, on slippery surfaces, intentional maneuvers, and roundabouts.
Main Results:
- Generated a comprehensive dataset of motorcycle falls and extreme maneuvers.
- The data captures sensor readings during realistic fall simulations.
- The dataset includes information on motorcycle handling at the limits of performance.
Conclusions:
- The presented dataset is valuable for developing and validating motorcycle fall detection systems.
- This data can improve the accuracy and reliability of algorithms predicting motorcycle accidents.
- The findings contribute to advancing motorcycle safety technologies.
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