Extraction of Stride Events From Gait Accelerometry During Treadmill Walking
| Autor: | Jennifer S. Brach, Subashan Perera, Jennica Bellanca, Kristin A. Lowry, Mark S. Redfern, Ervin Sejdic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
030506 rehabilitation
medicine.medical_specialty Heel lcsh:Medical technology Computer science stride intervals Biomedical Engineering STRIDE Gait accelerometry signals Accelerometer lcsh:Computer applications to medicine. Medical informatics gait Motion capture Article 03 medical and health sciences 0302 clinical medicine Gait (human) Physical medicine and rehabilitation medicine Treadmill signal processing Motion compensation General Medicine medicine.anatomical_structure lcsh:R855-855.5 Gait analysis Physical therapy lcsh:R858-859.7 0305 other medical science human activities 030217 neurology & neurosurgery |
| Zdroj: | IEEE Journal of Translational Engineering in Health and Medicine IEEE Journal of Translational Engineering in Health and Medicine, Vol 4, Pp 1-11 (2016) |
| ISSN: | 2168-2372 |
| Popis: | 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. Evaluating stride events are valuable for understanding the changes in walking due to aging and neurological diseases, but, 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. We proposed and validated a method to extract stride cycle events from gait accelerometry signals captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. The proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step towards the assessment of stride events using tri-axial accelerometers in real-life settings. |
| Databáze: | OpenAIRE |
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