Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan (2020)
Performing Realistic Workout Activity Recognition on Consumer Smartphones.
In: Technologies, 8 (4)
doi: 10.3390/technologies8040065
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone a powerful device for human activity recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. We already investigated the possibility of using an unmodified commercial smartphone to recognize eight strength-based exercises. App-based workouts have become popular in the last few years. The advantage of using a mobile device is that you can practice anywhere at anytime. In our previous work, we proved the possibility of turning a commercial smartphone into an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants, we showed the first results for cross person evaluation and the generalization ability of our inference models on disjoint participants. In this work, we extended another model to further improve the model generalizability and provided a thorough comparison of our proposed system to other existing state-of-the-art approaches. Finally, a concept of counting the repetitions is also provided in this study as a parallel task to classification.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Performing Realistic Workout Activity Recognition on Consumer Smartphones |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2020 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Technologies |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| (Heft-)Nummer: | 4 |
| DOI: | 10.3390/technologies8040065 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone a powerful device for human activity recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. We already investigated the possibility of using an unmodified commercial smartphone to recognize eight strength-based exercises. App-based workouts have become popular in the last few years. The advantage of using a mobile device is that you can practice anywhere at anytime. In our previous work, we proved the possibility of turning a commercial smartphone into an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants, we showed the first results for cross person evaluation and the generalization ability of our inference models on disjoint participants. In this work, we extended another model to further improve the model generalizability and provided a thorough comparison of our proposed system to other existing state-of-the-art approaches. Finally, a concept of counting the repetitions is also provided in this study as a parallel task to classification. |
| Freie Schlagworte: | Ultrasonic sensing, Mobile sensors, Human activity recognition, Proximity sensing |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 02 Dez 2020 12:17 |
| Letzte Änderung: | 03 Jul 2024 02:48 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Performing Realistic Workout Activity Recognition on Consumer Smartphones. (deposited 09 Feb 2022 14:43)
- Performing Realistic Workout Activity Recognition on Consumer Smartphones. (deposited 02 Dez 2020 12:17) [Gegenwärtig angezeigt]
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