Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units
Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy...
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4194 |
| _version_ | 1798024355788095488 |
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| author | Markus Zrenner Stefan Gradl Ulf Jensen Martin Ullrich Bjoern M. Eskofier |
| author_facet | Markus Zrenner Stefan Gradl Ulf Jensen Martin Ullrich Bjoern M. Eskofier |
| author_sort | Markus Zrenner |
| collection | DOAJ |
| description | Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe. |
| first_indexed | 2024-04-11T18:02:09Z |
| format | Article |
| id | doaj.art-8ff1215382f24316857d97ee1932c520 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:02:09Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ff1215382f24316857d97ee1932c5202022-12-22T04:10:27ZengMDPI AGSensors1424-82202018-11-011812419410.3390/s18124194s18124194Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement UnitsMarkus Zrenner0Stefan Gradl1Ulf Jensen2Martin Ullrich3Bjoern M. Eskofier4Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyMachine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyFinance & IT—IT Innovation, Adidas AG, 91074 Herzogenaurach, GermanyMachine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyMachine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, GermanyRunning has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.https://www.mdpi.com/1424-8220/18/12/4194wearable sensorsinertial measurement unitgaitrunningstride lengthvelocitysmart shoe |
| spellingShingle | Markus Zrenner Stefan Gradl Ulf Jensen Martin Ullrich Bjoern M. Eskofier Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units Sensors wearable sensors inertial measurement unit gait running stride length velocity smart shoe |
| title | Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units |
| title_full | Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units |
| title_fullStr | Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units |
| title_full_unstemmed | Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units |
| title_short | Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units |
| title_sort | comparison of different algorithms for calculating velocity and stride length in running using inertial measurement units |
| topic | wearable sensors inertial measurement unit gait running stride length velocity smart shoe |
| url | https://www.mdpi.com/1424-8220/18/12/4194 |
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