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

: Fu, Biying; Kirchbuchner, Florian; Kuijper, Arjan


Association for Computing Machinery -ACM-; National Science Foundation -NSF-:
PETRA 2020, 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Conference Proceedings : June 30 - July 3, 2020, Virtual Conference
New York: ACM, 2020
ISBN: 978-1-4503-7773-7
Art. 20, 10 S.
International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Online>
Fraunhofer IGD ()
Lead Topic: Individual Health; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); sensor data exploration; Mobile applications; assistive technologies; human action recognition

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.