ArticleFrontiers in neuroscience2024
Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The psychometric properties of Functional Gait Assessment (FGA) in assessing walking ability in people with knee osteoarthritis.Journal of exercise science and fitness · 2026Article
- [Personalized lower-limb gait assessment method based on musculoskeletal modeling and machine learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Deep Learning-Based Automated Clinical Gait Assessment From Kinematic Data in People With Stroke: Development and Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- 100 Normative Gait Profiles with 5-year fall tracking: Benchmark Dataset for Southeast Asian Movement Science.Scientific data · 2026Article
- Observational
- Characteristics of Ground Reaction Force Variability During Walking in Post-Stroke Patients.Sensors (Basel, Switzerland) · 2025Article
- Quantifying Gait Asymmetry in Stroke Patients: A Statistical Parametric Mapping (SPM) Approach.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
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Authors and funding
10 authors.
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Abstract
Background: This study aimed to identify and quantify the kinematic and kinetic gait deviations in post-stroke hemiplegic patients with matched healthy controls using Statistical Parametric Mapping (SPM). Methods: Fifteen chronic stroke patients [4 females, 11 males; age 53.7 (standard deviation 12.2) years; body mass 65.4 (10.4) kg; standing height 168.5 (9.6) cm] and 15 matched healthy controls [4 females, 11 males; age 52.9 (11.7) years; body weight 66.5 (10.7) years; standing height 168.3 (8.8) cm] were recruited. In a 10-m walking task, joint angles, ground reaction forces (GRF), and joint moments were collected, analyzed, and compared using SPM for an entire gait cycle. Results: Generally, when comparing the stroke patients' affected (hemiplegic) and less-affected (contralateral) limbs with the control group, SPM identified significant differences in the late stance phase and early swing phase in the joint angles and moments in bilateral limbs (all Conclusion: SPM was able to detect abnormal gait patterns in both the affected and less-affected limbs of stroke patients with significant differences when compared with matched controls. The findings draw attention to significant quantifiable gait deviations in the less-affected post-stroke limb with the potential impact to inform gait retraining strategies for clinicians and physiotherapists.
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