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The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.

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Title: The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.
Authors: Cleland, Brice Thomas1 (AUTHOR), Kim, Madeline1 (AUTHOR), Madhavan, Sangeetha1 (AUTHOR) smadhava@uic.edu
Source: Annals of Biomedical Engineering. Aug2025, Vol. 53 Issue 8, p1890-1902. 13p.
Subjects: Walking speed, Wearable technology, Physical fitness, Physical mobility, Animal locomotion, Stroke rehabilitation, Kinematics, Gait disorders
Abstract: Purpose: After stroke, walking is characterized by hemiparetic patterns, quantified with force sensitive walkways and motion capture systems. Some joint-level kinematic patterns of walking also can be obtained with wearable sensors. The purpose of this project was to measure joint-level kinematic patterns during walking with wearable sensors and determine the association with walking speed and endurance in individuals with chronic stroke. Methods: In this cross-sectional observational study, participants donned APDM Opal wearable sensors during walking tests (10-meter walk test or 6-min walk test). We extracted joint-level kinematic variables of elevation at midswing, circumduction, foot strike angle, and toe-off angle. Associations of each variable with walking speed and endurance were tested, and significantly associated variables were entered into a regression model. Results: 68 individuals with chronic stroke were included. We found that the less affected foot strike angle, less affected toe-off angle, and more affected toe-off angle were significant predictors of walking speed (R2 ≥ 0.71, p < 0.001). Less affected toe-off angle, more affected foot strike angle, and more affected toe-off angle were significant predictors of walking endurance (R2 ≥ 0.67, p < 0.001). Conclusion: We found consistent evidence that greater toe-off angle (may reflect greater push-off) and lesser foot strike angle (may reflect lesser foot drop) were important predictors of greater walking speed and endurance. Our results suggest that wearable sensors can provide important information about joint-level kinematic patterns that are important for walking function. This information could help therapists target interventions toward specific deficits or compensatory patterns to improve walking. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Annals+of+Biomedical+Engineering%22&quot;&gt;Annals of Biomedical Engineering&lt;/searchLink&gt;. Aug2025, Vol. 53 Issue 8, p1890-1902. 13p.
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– Name: Abstract
  Label: Abstract
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  Data: Purpose: After stroke, walking is characterized by hemiparetic patterns, quantified with force sensitive walkways and motion capture systems. Some joint-level kinematic patterns of walking also can be obtained with wearable sensors. The purpose of this project was to measure joint-level kinematic patterns during walking with wearable sensors and determine the association with walking speed and endurance in individuals with chronic stroke. Methods: In this cross-sectional observational study, participants donned APDM Opal wearable sensors during walking tests (10-meter walk test or 6-min walk test). We extracted joint-level kinematic variables of elevation at midswing, circumduction, foot strike angle, and toe-off angle. Associations of each variable with walking speed and endurance were tested, and significantly associated variables were entered into a regression model. Results: 68 individuals with chronic stroke were included. We found that the less affected foot strike angle, less affected toe-off angle, and more affected toe-off angle were significant predictors of walking speed (R2 ≥ 0.71, p &lt; 0.001). Less affected toe-off angle, more affected foot strike angle, and more affected toe-off angle were significant predictors of walking endurance (R2 ≥ 0.67, p &lt; 0.001). Conclusion: We found consistent evidence that greater toe-off angle (may reflect greater push-off) and lesser foot strike angle (may reflect lesser foot drop) were important predictors of greater walking speed and endurance. Our results suggest that wearable sensors can provide important information about joint-level kinematic patterns that are important for walking function. This information could help therapists target interventions toward specific deficits or compensatory patterns to improve walking. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: &lt;i&gt;Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1007/s10439-025-03754-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1890
    Subjects:
      – SubjectFull: Walking speed
        Type: general
      – SubjectFull: Wearable technology
        Type: general
      – SubjectFull: Physical fitness
        Type: general
      – SubjectFull: Physical mobility
        Type: general
      – SubjectFull: Animal locomotion
        Type: general
      – SubjectFull: Stroke rehabilitation
        Type: general
      – SubjectFull: Kinematics
        Type: general
      – SubjectFull: Gait disorders
        Type: general
    Titles:
      – TitleFull: The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors.
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            NameFull: Cleland, Brice Thomas
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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