Classification and Prediction of Bicycle-Road-Quality using IMU Data
- The present work ties in with the problem of bicycle road assessment that is currently done using expensive special measuring vehicles. Our alternative approach for road condition assessment is to mount a sensor device on a bicycle which sends accelerometer and gyroscope data via WiFi to a classification server. There, a prediction model determines road type and condition based on the sensor data.The present work ties in with the problem of bicycle road assessment that is currently done using expensive special measuring vehicles. Our alternative approach for road condition assessment is to mount a sensor device on a bicycle which sends accelerometer and gyroscope data via WiFi to a classification server. There, a prediction model determines road type and condition based on the sensor data. For the classification task, we compare different machine learning methods with each other, whereby validation accuracies of 99% can be achieved with deep residual networks such as InceptionTime. The main contribution of this work with respect to comparable work is that we achieve excellent accuracies on a realistic dataset classifying road conditions into nine distinct classes that are highly relevant for practice.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/5380 | Bibliografische Angaben |
Title (English): | Classification and Prediction of Bicycle-Road-Quality using IMU Data |
Conference: | The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2021), 27th October 2021, Kaiserslautern |
Author: | Johannes Heidt, Klaus DorerStaff MemberORCiDGND |
Year of Publication: | 2021 |
Creating Corporation: | Hochschule Kaiserslautern |
Page Number: | 12 |
First Page: | 138 |
Last Page: | 149 |
Parent Title (English): | Artificial Intelligence : Application in Life Sciences and Beyond |
Editor: | Karl-Herbert Schäfer, Franz Quint |
URN: | https://urn:nbn:de:bsz:ofb1-opus4-53809 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Forschung / IMLA - Institute for Machine Learning and Analytics | |
Institutes: | Bibliografie |
Projekte / Magma Offenburg | |
Tag: | Deep Learning; InceptionTime; Machine Learning; ResNet; Road-Quality Prediction; Time-series Classification | Formale Angaben |
Open Access: | Open Access |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
ArXiv Id: | http://arxiv.org/abs/2112.05657 |