Estimating Stair Running Performance Using Inertial Sensors
tair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based m...
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| Format: | Article |
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MDPI AG
2018
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| Online Access: | http://hdl.handle.net/1721.1/114487 https://orcid.org/0000-0002-0119-1617 |
| _version_ | 1811089697109180416 |
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| author | Ojeda, Lauro Zaferiou, Antonia Cain, Stephen Vitali, Rachel Davidson, Steven Perkins, Noel Stirling, Leia A. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Ojeda, Lauro Zaferiou, Antonia Cain, Stephen Vitali, Rachel Davidson, Steven Perkins, Noel Stirling, Leia A. |
| author_sort | Ojeda, Lauro |
| collection | MIT |
| description | tair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. |
| first_indexed | 2024-09-23T14:23:16Z |
| format | Article |
| id | mit-1721.1/114487 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:23:16Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1144872022-10-01T21:02:29Z Estimating Stair Running Performance Using Inertial Sensors Ojeda, Lauro Zaferiou, Antonia Cain, Stephen Vitali, Rachel Davidson, Steven Perkins, Noel Stirling, Leia A. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Stirling, Leia A. tair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. United States. Army. Army Contracting Command 2018-03-30T18:45:53Z 2018-03-30T18:45:53Z 2017-11 2017-11 2018-03-02T16:16:52Z Article http://purl.org/eprint/type/JournalArticle 1424-8220 http://hdl.handle.net/1721.1/114487 Ojeda, Lauro, Antonia Zaferiou, Stephen Cain, Rachel Vitali, Steven Davidson, Leia Stirling, and Noel Perkins. “Estimating Stair Running Performance Using Inertial Sensors.” Sensors 17, no. 12 (November 17, 2017): 2647. © 2017 by the Authors https://orcid.org/0000-0002-0119-1617 http://dx.doi.org/10.3390/s17112647 Sensors Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG Diversity |
| spellingShingle | Ojeda, Lauro Zaferiou, Antonia Cain, Stephen Vitali, Rachel Davidson, Steven Perkins, Noel Stirling, Leia A. Estimating Stair Running Performance Using Inertial Sensors |
| title | Estimating Stair Running Performance Using Inertial Sensors |
| title_full | Estimating Stair Running Performance Using Inertial Sensors |
| title_fullStr | Estimating Stair Running Performance Using Inertial Sensors |
| title_full_unstemmed | Estimating Stair Running Performance Using Inertial Sensors |
| title_short | Estimating Stair Running Performance Using Inertial Sensors |
| title_sort | estimating stair running performance using inertial sensors |
| url | http://hdl.handle.net/1721.1/114487 https://orcid.org/0000-0002-0119-1617 |
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