Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking
<italic>Goal:</italic> Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To add...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10411039/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583234480766976 |
|---|---|
| author | Arne Kuderle Martin Ullrich Nils Roth Malte Ollenschlager Alzhraa A. Ibrahim Hamid Moradi Robert Richer Ann-Kristin Seifer Matthias Zurl Raul C. Simpetru Liv Herzer Dominik Prossel Felix Kluge Bjoern M. Eskofier |
| author_facet | Arne Kuderle Martin Ullrich Nils Roth Malte Ollenschlager Alzhraa A. Ibrahim Hamid Moradi Robert Richer Ann-Kristin Seifer Matthias Zurl Raul C. Simpetru Liv Herzer Dominik Prossel Felix Kluge Bjoern M. Eskofier |
| author_sort | Arne Kuderle |
| collection | DOAJ |
| description | <italic>Goal:</italic> Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. <italic>Methods:</italic> This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. <italic>Conclusion:</italic> The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond. |
| format | Article |
| id | doaj-art-7c1d2ffe1462458aa9a4e12090a5e3e2 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-7c1d2ffe1462458aa9a4e12090a5e3e22025-01-29T00:01:26ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01516317210.1109/OJEMB.2024.335679110411039Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm BenchmarkingArne Kuderle0https://orcid.org/0000-0002-5686-281XMartin Ullrich1https://orcid.org/0000-0001-7348-6097Nils Roth2https://orcid.org/0000-0002-9166-3920Malte Ollenschlager3https://orcid.org/0000-0002-8135-6740Alzhraa A. Ibrahim4https://orcid.org/0000-0003-3308-061XHamid Moradi5https://orcid.org/0009-0001-2715-8591Robert Richer6https://orcid.org/0000-0003-0272-5403Ann-Kristin Seifer7https://orcid.org/0000-0002-5891-218XMatthias Zurl8https://orcid.org/0000-0002-9678-166XRaul C. Simpetru9https://orcid.org/0000-0003-0455-0168Liv Herzer10https://orcid.org/0009-0007-3427-5132Dominik Prossel11Felix Kluge12https://orcid.org/0000-0003-4921-6104Bjoern M. Eskofier13https://orcid.org/0000-0002-0417-0336Machine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany<italic>Goal:</italic> Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. <italic>Methods:</italic> This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. <italic>Conclusion:</italic> The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.https://ieeexplore.ieee.org/document/10411039/Accelerometerwalkingbiomarkerbiomechanicsmovement analysis |
| spellingShingle | Arne Kuderle Martin Ullrich Nils Roth Malte Ollenschlager Alzhraa A. Ibrahim Hamid Moradi Robert Richer Ann-Kristin Seifer Matthias Zurl Raul C. Simpetru Liv Herzer Dominik Prossel Felix Kluge Bjoern M. Eskofier Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking IEEE Open Journal of Engineering in Medicine and Biology Accelerometer walking biomarker biomechanics movement analysis |
| title | Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking |
| title_full | Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking |
| title_fullStr | Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking |
| title_full_unstemmed | Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking |
| title_short | Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking |
| title_sort | gaitmap x2014 an open ecosystem for imu based human gait analysis and algorithm benchmarking |
| topic | Accelerometer walking biomarker biomechanics movement analysis |
| url | https://ieeexplore.ieee.org/document/10411039/ |
| work_keys_str_mv | AT arnekuderle gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT martinullrich gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT nilsroth gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT malteollenschlager gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT alzhraaaibrahim gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT hamidmoradi gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT robertricher gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT annkristinseifer gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT matthiaszurl gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT raulcsimpetru gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT livherzer gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT dominikprossel gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT felixkluge gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking AT bjoernmeskofier gaitmapx2014anopenecosystemforimubasedhumangaitanalysisandalgorithmbenchmarking |