Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning

Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement sy...

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Main Authors: Bernhard Hollaus, Jasper C. Volmer, Thomas Fleischmann
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/22/16/6140
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author Bernhard Hollaus
Jasper C. Volmer
Thomas Fleischmann
author_facet Bernhard Hollaus
Jasper C. Volmer
Thomas Fleischmann
author_sort Bernhard Hollaus
collection DOAJ
description Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network’s aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist.
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spelling doaj.art-b1d7127aae514b8d9d5d78b930279c822023-12-03T14:26:43ZengMDPI AGSensors1424-82202022-08-012216614010.3390/s22166140Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine LearningBernhard Hollaus0Jasper C. Volmer1Thomas Fleischmann2Department of Medical, Health & Sports Engineering, Management Center Innsbruck, 6020 Innsbruck, AustriaDepartment of Mechatronics, Management Center Innsbruck, 6020 Innsbruck, AustriaDepartment of Medical, Health & Sports Engineering, Management Center Innsbruck, 6020 Innsbruck, AustriaMost commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network’s aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist.https://www.mdpi.com/1424-8220/22/16/6140sensor platformcadencewearablemachine learningconvolutional neural networkroad cycling
spellingShingle Bernhard Hollaus
Jasper C. Volmer
Thomas Fleischmann
Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
Sensors
sensor platform
cadence
wearable
machine learning
convolutional neural network
road cycling
title Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
title_full Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
title_fullStr Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
title_full_unstemmed Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
title_short Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning
title_sort cadence detection in road cycling using saddle tube motion and machine learning
topic sensor platform
cadence
wearable
machine learning
convolutional neural network
road cycling
url https://www.mdpi.com/1424-8220/22/16/6140
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AT thomasfleischmann cadencedetectioninroadcyclingusingsaddletubemotionandmachinelearning