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Fitness Activity Recognition on Smartphones Using Doppler Measurements

Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan ; Braun, Andreas ; Vaithyalingam Gangatharan, Dinesh (2023)
Fitness Activity Recognition on Smartphones Using Doppler Measurements.
In: Informatics, 2018, 5 (2)
doi: 10.26083/tuprints-00016027
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: Fitness Activity Recognition on Smartphones Using Doppler Measurements
Language: English
Date: 1 December 2023
Place of Publication: Darmstadt
Year of primary publication: 2018
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Informatics
Volume of the journal: 5
Issue Number: 2
Collation: 14 Seiten
DOI: 10.26083/tuprints-00016027
Corresponding Links:
Origin: Secondary publication DeepGreen

Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform fitness activities, or increase the amount of sports exercise. Thus far, most applications rely on accelerometers or gyroscopes that are integrated into the devices. They have to be worn on the body to track activities. In this work, we investigated the use of a speaker and a microphone that are integrated into a smartphone to track exercises performed close to it. We combined active sonar and Doppler signal analysis in the ultrasound spectrum that is not perceivable by humans. We wanted to measure the body weight exercises bicycles, toe touches, and squats, as these consist of challenging radial movements towards the measuring device. We have tested several classification methods, ranging from support vector machines to convolutional neural networks. We achieved an accuracy of 88% for bicycles, 97% for toe-touches and 91% for squats on our test set.

Uncontrolled Keywords: human activity recognition, exercise recognition, mobile sensing, ultrasound sensing, Doppler effect
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-160278
Additional Information:

This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Fraunhofer IGD
Date Deposited: 01 Dec 2023 13:52
Last Modified: 09 Jan 2024 09:00
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/16027
PPN: 514513535
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