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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.
In: Technologies, 2022, 8 (4)
doi: 10.26083/tuprints-00017432
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: Performing Realistic Workout Activity Recognition on Consumer Smartphones
Language: English
Date: 9 February 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Technologies
Volume of the journal: 8
Issue Number: 4
Collation: 17 Seiten
DOI: 10.26083/tuprints-00017432
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: ubiquitous sensing, ultrasonic sensing, mobile sensing, human activity recognition, proximity sensing, exercise recognition
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-174321
Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 09 Feb 2022 14:43
Last Modified: 14 Nov 2023 19:03
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17432
PPN: 50558669X
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