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Performing Realistic Workout Activity Recognition on Consumer Smartphones

Fu, Biying ; Kirchbuchner, Florian ; Kuijper, Arjan (2022):
Performing Realistic Workout Activity Recognition on Consumer Smartphones. (Publisher's Version)
In: Technologies, 8 (4), MDPI, e-ISSN 2227-7080,
DOI: 10.26083/tuprints-00017432,

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

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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Performing Realistic Workout Activity Recognition on Consumer Smartphones
Language: English

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.

Journal or Publication Title: Technologies
Volume of the journal: 8
Issue Number: 4
Publisher: MDPI
Collation: 17 Seiten
Uncontrolled Keywords: ubiquitous sensing, ultrasonic sensing, mobile sensing, human activity recognition, proximity sensing, exercise recognition
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Divisions: 20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 09 Feb 2022 14:43
Last Modified: 02 May 2022 12:07
DOI: 10.26083/tuprints-00017432
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-174321
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17432
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