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Fitness Activity Recognition on Smartphones Using Doppler Measurements

1
Fraunhofer IGD, 64283 Darmstadt, Germany
2
Computer Science, Technische Universität Darmstadt, 64283 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Current address: Fraunhoferstrasse 5, 64283 Darmstadt, Germany.
Informatics 2018, 5(2), 24; https://doi.org/10.3390/informatics5020024
Received: 27 February 2018 / Revised: 29 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform fitness activities, or increase the amount of sports exercise. Thus far, most applications rely on accelerometers or gyroscopes that are integrated into the devices. They have to be worn on the body to track activities. In this work, we investigated the use of a speaker and a microphone that are integrated into a smartphone to track exercises performed close to it. We combined active sonar and Doppler signal analysis in the ultrasound spectrum that is not perceivable by humans. We wanted to measure the body weight exercises bicycles, toe touches, and squats, as these consist of challenging radial movements towards the measuring device. We have tested several classification methods, ranging from support vector machines to convolutional neural networks. We achieved an accuracy of 88% for bicycles, 97% for toe-touches and 91% for squats on our test set. View Full-Text
Keywords: human activity recognition; exercise recognition; mobile sensing; ultrasound sensing; Doppler effect human activity recognition; exercise recognition; mobile sensing; ultrasound sensing; Doppler effect
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MDPI and ACS Style

Fu, B.; Kirchbuchner, F.; Kuijper, A.; Braun, A.; Vaithyalingam Gangatharan, D. Fitness Activity Recognition on Smartphones Using Doppler Measurements. Informatics 2018, 5, 24.

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