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
Artikel, Zweitveröffentlichung, Verlagsversion
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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: | 2023 |
| Autor(en): | Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan ; Braun, Andreas ; Vaithyalingam Gangatharan, Dinesh |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Fitness Activity Recognition on Smartphones Using Doppler Measurements |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Dezember 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2018 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Informatics |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | 2 |
| Kollation: | 14 Seiten |
| DOI: | 10.26083/tuprints-00016027 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/16027 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| 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: | human activity recognition, exercise recognition, mobile sensing, ultrasound sensing, Doppler effect |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-160278 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Fraunhofer IGD |
| Hinterlegungsdatum: | 01 Dez 2023 13:52 |
| Letzte Änderung: | 04 Dez 2023 11:59 |
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| Suche nach Titel in: | TUfind oder in Google |
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