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Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones

Fu, Biying and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones.
p. 10, ACM, 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA'20), Corfu, Greece, June 2020, ISBN 978-1-4503-7773-7,
DOI: 10.1145/3389189.3389195,
[Conference or Workshop Item]

Abstract

Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone even more powerful than the computers NASA used to send astronauts to the moon. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. In this paper, we investigate the possibility of using a unmodified commercial off-the-shelf (COTS) smartphone to recognize 8 different workout exercises. App-based workout has become popular in the last few years. People do not need to go to the gym to practice. The advantage of using a mobile device is, that you can practice anywhere at anytime. In this work, we turned a COTS smartphone to an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants performing these eight exercises, we show first results for cross person evaluation and the generalization ability of our inference models on unseen participants. A bidirectional LSTM model achieved an overall F1 score of 88.86 % for the cross subject case and 79.52 % for the holdout participants evaluation. Similar good results can be achieved by a VGG16 fine-tuned model in comparison to a 2D-CNN architecture trained from scratch.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Fu, Biying and Kirchbuchner, Florian and Kuijper, Arjan
Title: Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones
Language: English
Abstract:

Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone even more powerful than the computers NASA used to send astronauts to the moon. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. In this paper, we investigate the possibility of using a unmodified commercial off-the-shelf (COTS) smartphone to recognize 8 different workout exercises. App-based workout has become popular in the last few years. People do not need to go to the gym to practice. The advantage of using a mobile device is, that you can practice anywhere at anytime. In this work, we turned a COTS smartphone to an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants performing these eight exercises, we show first results for cross person evaluation and the generalization ability of our inference models on unseen participants. A bidirectional LSTM model achieved an overall F1 score of 88.86 % for the cross subject case and 79.52 % for the holdout participants evaluation. Similar good results can be achieved by a VGG16 fine-tuned model in comparison to a 2D-CNN architecture trained from scratch.

Publisher: ACM
ISBN: 978-1-4503-7773-7
Uncontrolled Keywords: Sensor data exploration, Mobile applications, Assistive technologies, Human action recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA'20)
Event Location: Corfu, Greece
Event Dates: June 2020
Date Deposited: 26 Oct 2020 12:15
DOI: 10.1145/3389189.3389195
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