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Exercise monitoring on consumer smart phones using ultrasonic sensing

: Fu, Biying; Gangatharan, Dinesh Vaithyalingam; Kuijper, Arjan; Kirchbuchner, Florian; Braun, Andreas


Yordanova, Kristina (Ed.); Schröder, M.; Bader, S.; Kirste, Thomas ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Computer and Human Interaction -SIGCHI-; Fraunhofer-Institut für Graphische Datenverarbeitung -IGD-, Darmstadt:
iWOAR 2017, 4th International Workshop on Sensor-based Activity Recognition and Interaction : 21. - 22. September 2017, Rostock
New York: ACM Press, 2017 (ACM International Conference Proceedings Series 1183)
ISBN: 978-1-4503-5223-9
Art. 9, 6 pp.
International Workshop on Sensor-based Activity Recognition (iWOAR) <4, 2017, Rostock>
Conference Paper
Fraunhofer IGD ()
mobile application; user interface; input device; Guiding Theme: Individual Health; Research Area: Human computer interaction (HCI)

Quantified self has been a trend over the last several years. An increasing number of people use devices, such as smartwatches or smartphones to log activities of daily life, including step count or vital information. However, most of these devices have to be worn by the user during the activities, as they rely on integrated motion sensors. Our goal is to create a technology that enables similar precision with remote sensing, based on common sensors installed in every smartphone, in order to enable ubiquitous application. We have created a system that uses the Doppler effect in ultrasound frequencies to detect motion around the smartphone. We propose a novel use case to track exercises, based on several feature extraction methods and machine learning classification. We conducted a study with 14 users, achieving an accuracy between 73% and 92% for the different exercises.