Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan ; Braun, Andreas ; Vaithyalingam Gangatharan, Dinesh (2018)
Fitness Activity Recognition on Smartphones Using Doppler Measurements.
In: Informatics, 5 (2)
doi: 10.3390/informatics5020024
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan ; Braun, Andreas ; Vaithyalingam Gangatharan, Dinesh |
| Art des Eintrags: | Bibliographie |
| Titel: | Fitness Activity Recognition on Smartphones Using Doppler Measurements |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Juni 2018 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Informatics |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | 2 |
| Kollation: | 14 Seiten |
| DOI: | 10.3390/informatics5020024 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Mobile sensors, Mobile applications, User interfaces, Input devices, Human activity recognition |
| Zusätzliche Informationen: | This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction ; Art.No.: 24 ; Erstveröffentlichung |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Fraunhofer IGD 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 26 Jun 2019 11:43 |
| Letzte Änderung: | 04 Dez 2023 11:58 |
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| Zugehörige Links: | |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Fitness Activity Recognition on Smartphones Using Doppler Measurements. (deposited 01 Dez 2023 13:52)
- Fitness Activity Recognition on Smartphones Using Doppler Measurements. (deposited 26 Jun 2019 11:43) [Gegenwärtig angezeigt]
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