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Article

Performing Realistic Workout Activity Recognition on Consumer Smartphones

1
Fraunhofer Institute for Computer Graphics Research IGD, 64283 Darmstadt, Germany
2
Mathematical and Applied Visual Computing, TU Darmstadt, 64283 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Technologies 2020, 8(4), 65; https://doi.org/10.3390/technologies8040065
Received: 16 July 2020 / Revised: 22 October 2020 / Accepted: 2 November 2020 / Published: 6 November 2020
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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. View Full-Text
Keywords: ubiquitous sensing; ultrasonic sensing; mobile sensing; human activity recognition; proximity sensing; exercise recognition ubiquitous sensing; ultrasonic sensing; mobile sensing; human activity recognition; proximity sensing; exercise recognition
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MDPI and ACS Style

Fu, B.; Kirchbuchner, F.; Kuijper, A. Performing Realistic Workout Activity Recognition on Consumer Smartphones. Technologies 2020, 8, 65. https://doi.org/10.3390/technologies8040065

AMA Style

Fu B, Kirchbuchner F, Kuijper A. Performing Realistic Workout Activity Recognition on Consumer Smartphones. Technologies. 2020; 8(4):65. https://doi.org/10.3390/technologies8040065

Chicago/Turabian Style

Fu, Biying, Florian Kirchbuchner, and Arjan Kuijper. 2020. "Performing Realistic Workout Activity Recognition on Consumer Smartphones" Technologies 8, no. 4: 65. https://doi.org/10.3390/technologies8040065

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