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Extraction of Stride Events From Gait Accelerometry During Treadmill Walking

Sejdic, Ervin and Lowry, Kristin A. and Bellanca, Jennica and Perera, Subashan and Redfern, Mark S. and Brach, Jennifer S. (2016) Extraction of Stride Events From Gait Accelerometry During Treadmill Walking. IEEE Journal of Translational Engineering in Health and Medicine, 4. pp. 1-11. ISSN 2168-2372

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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 accelerometer 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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lowry, Kristin A.
Bellanca, Jennica
Redfern, Mark
Brach, Jennifer S.
Date: 2 December 2016
Date Type: Publication
Journal or Publication Title: IEEE Journal of Translational Engineering in Health and Medicine
Volume: 4
Publisher: Institute of Electrical and Electronics Engineers
Page Range: pp. 1-11
DOI or Unique Handle: 10.1109/jtehm.2015.2504961
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Refereed: Yes
ISSN: 2168-2372
Official URL:
Article Type: Research Article
Date Deposited: 13 May 2020 16:41
Last Modified: 13 May 2020 16:41


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