Quantifying Physical Activity Through Step Count Estimation Across Multiple Wearable Sensor Configurations: A Validation Study in Post-Stroke and Healthy Adults.
Objective: The study aimed to identify the most accurate protocol for measuring stride-count in post-stroke and healthy individuals, by comparing different wearable-sensors, their placement and data processing against a gold-standard instrumented treadmill. Methods: Eighteen post-stroke and 18 healt...
| Publicado en: | NeuroRehabilitation Vol. 59; no. 2; pp. 249 - 268 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196473406&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196473406 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10538135 3RE jtl: NeuroRehabilitation issn: 10538135 maglogo: N pubinfo: dt: Sep2026 vid: 59 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 196473406 195200918 196473406 196473406 10.1177/10538135261463255 196473406 ppf: 249 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Quantifying Physical Activity Through Step Count Estimation Across Multiple Wearable Sensor Configurations: A Validation Study in Post-Stroke and Healthy Adults. aug: au: Macq, Louise Dehem, Stéphanie Lejeune, Thierry Otlet, Virginie affil: IREC, Institute of Experimental and Clinical Research, UCLouvain Bruxelles-Woluwe, Woluwe-St-Lambert, Belgium sug: subj: Stroke Rehabilitation Physical Activity Evaluation Step Actigraphy Methods Wearable Sensors Protocols Human Comparative Studies Funding Source Treadmills Accelerometers Stroke Patients Walking Speed Correlation Coefficient Algorithms ab: Objective: The study aimed to identify the most accurate protocol for measuring stride-count in post-stroke and healthy individuals, by comparing different wearable-sensors, their placement and data processing against a gold-standard instrumented treadmill. Methods: Eighteen post-stroke and 18 healthy adults walked at multiple speeds on an instrumented treadmill equipped with force plates. Participants wore six ActiGraph accelerometers (wrists, hips, ankles), and pressure insoles. Stride counts from each sensor configuration were estimated using a custom raw-acceleration peak-detection algorithm and the manufacturer's algorithm (ActiLife®). Accuracy and agreement with the gold standard were assessed across walking speeds and sensor locations using the concordance correlation coefficient (CCC) and linear mixed-effects model. Results: Stride count accuracy was influenced by walking speed across all wearable configurations, improving with gait velocity. Pressure-sensing insoles demonstrated the highest accuracy and agreement with the gold standard in both groups across speeds (CCC = 0.999 for healthy subjects; 0.996 for post-stroke). The custom peak-detection algorithm applied to ankle-worn accelerometers provided accurate and robust stride estimates across speeds (CCC = 0.986 in healthy subjects; 0.990 post-stroke). In contrast, the manufacturer's algorithm consistently misestimated stride counts in both groups. No side-to-side differences were observed for any sensor placement. Conclusion: Pressure insoles and our custom raw-acceleration algorithm applied to ankle-worn accelerometers yielded the highest stride-count accuracy across walking speeds in both populations. Trial registry name and URL: ClinicalTrails.gov (Registration ID: NCT06943014) pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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