Development and Prospective Validation of Wearable Sensor-Based Gait Metric for Individuals with Lower-Limb Amputation.

Christopher Bennett, Ignacio Gaunaurd, Allison Symsack, E Brooks Applegate, Josué de León Santana, Paul Pasquina, Robert Gailey

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Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Christopher BennettDepartment of Music Engineering, Frost School of Music, University of Miami, Coral Gables, FL 33146, USA.ORCID 0000-0002-1150-7400

Ignacio GaunaurdDepartment of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.ORCID 0000-0001-8241-0684

Allison SymsackDepartment of Physical Medicine & Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.ORCID 0009-0004-3199-1027

E Brooks ApplegateDepartment of Education Leadership, Research, and Technology, Western Michigan University, Kalamazoo, WI 49008, USA.ORCID 0000-0002-3020-6224

Josué de León SantanaDepartment of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.ORCID 0009-0002-1595-1186

Paul PasquinaDepartment of Physical Medicine & Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.ORCID 0000-0003-2898-0082

Robert GaileyDepartment of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.ORCID 0000-0002-8512-2511

Funding

United States Department of Defense FAIN HU00012220039

6 · The paper itself

Abstract

Lower-limb amputation is associated with persistent gait asymmetries and functional limitations that are not fully captured by conventional clinical outcome measures. This study aimed to develop and prospectively validate a wearable sensor-based Gait Goodness Score (GGS) derived from ensemble classifiers to summarize overall gait quality during supervised clinical walking. The algorithm was previously trained using inertial measurement unit data and clinically meaningful temporal-spatial features. In the present prospective, observational validation study, medically stable adults with lower-limb amputation performed supervised 10 m walk tests in outpatient rehabilitation settings, during which step-based GGS values were collected. Associations between GGS and established clinical measures, including walking velocity, Amputee Mobility Predictor (AMP) score, and Timed Up and Go (TUG) durations, were examined. GGS demonstrated significant differences across functional levels and amputation levels and showed strong associations with walking velocity and AMP score, with a significant moderate-to-fair association also observed for TUG and PLUS-M. These findings support the validity of GGS as a quantitative, sensor-derived metric of gait quality during supervised clinical walking in individuals with lower-limb amputation.

Indexed as

Amputation, SurgicalBiosensing TechniquesGaitLower ExtremityWearable Electronic DevicesAdultAgedAlgorithmsAmputeesFemaleHumansMaleMiddle AgedProspective StudiesWalkinggait analysisinertial measurement unitslower-limb amputationmachine learningwearable sensors

Identifiers

PMID42515495

DOI10.3390/s26144613

PMCPMC13419156

What Socratic holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.