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Fitness Activity Recognition on Smartphones Using Doppler Measurements

Fu, Biying and Kirchbuchner, Florian and Kuijper, Arjan and Braun, Andreas and Vaithyalingam Gangatharan, Dinesh (2018):
Fitness Activity Recognition on Smartphones Using Doppler Measurements.
In: Informatics, p. 24, 5, (2), ISSN 2227-9709,
DOI: 10.3390/informatics5020024,
[Online-Edition: https://doi.org/10.3390/informatics5020024],
[Article]

Abstract

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.

Item Type: Article
Erschienen: 2018
Creators: Fu, Biying and Kirchbuchner, Florian and Kuijper, Arjan and Braun, Andreas and Vaithyalingam Gangatharan, Dinesh
Title: Fitness Activity Recognition on Smartphones Using Doppler Measurements
Language: English
Abstract:

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.

Journal or Publication Title: Informatics
Volume: 5
Number: 2
Uncontrolled Keywords: Mobile sensors, Mobile applications, User interfaces, Input devices, Human activity recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 26 Jun 2019 11:43
DOI: 10.3390/informatics5020024
Official URL: https://doi.org/10.3390/informatics5020024
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