Extraction of Stride Events From Gait Accelerometry During Treadmill Walking
Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles ac...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2016-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7343737/ |
| _version_ | 1823937272643321856 |
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| author | Ervin Sejdic Kristin A. Lowry Jennica Bellanca Subashan Perera Mark S. Redfern Jennifer S. Brach |
| author_facet | Ervin Sejdic Kristin A. Lowry Jennica Bellanca Subashan Perera Mark S. Redfern Jennifer S. Brach |
| author_sort | Ervin Sejdic |
| collection | DOAJ |
| description | Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings. |
| first_indexed | 2024-12-17T02:00:05Z |
| format | Article |
| id | doaj.art-db5a522bf5e64797a072f420927c2149 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-12-17T02:00:05Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-db5a522bf5e64797a072f420927c21492022-12-21T22:07:51ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722016-01-01411110.1109/JTEHM.2015.25049617343737Extraction of Stride Events From Gait Accelerometry During Treadmill WalkingErvin Sejdic0https://orcid.org/0000-0003-4987-8298Kristin A. Lowry1Jennica Bellanca2Subashan Perera3Mark S. Redfern4Jennifer S. Brach5Department of Electrical and Computer EngineeringSwanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USADepartment of Physical Therapy, Des Moines University, Des Moines, IA, USADepartment of BioengineeringSwanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USADivision of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USADepartment of BioengineeringSwanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USADepartment of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USAObjective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings.https://ieeexplore.ieee.org/document/7343737/Gait accelerometry signalsstride intervalssignal processinggait |
| spellingShingle | Ervin Sejdic Kristin A. Lowry Jennica Bellanca Subashan Perera Mark S. Redfern Jennifer S. Brach Extraction of Stride Events From Gait Accelerometry During Treadmill Walking IEEE Journal of Translational Engineering in Health and Medicine Gait accelerometry signals stride intervals signal processing gait |
| title | Extraction of Stride Events From Gait Accelerometry During Treadmill Walking |
| title_full | Extraction of Stride Events From Gait Accelerometry During Treadmill Walking |
| title_fullStr | Extraction of Stride Events From Gait Accelerometry During Treadmill Walking |
| title_full_unstemmed | Extraction of Stride Events From Gait Accelerometry During Treadmill Walking |
| title_short | Extraction of Stride Events From Gait Accelerometry During Treadmill Walking |
| title_sort | extraction of stride events from gait accelerometry during treadmill walking |
| topic | Gait accelerometry signals stride intervals signal processing gait |
| url | https://ieeexplore.ieee.org/document/7343737/ |
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