How many strides are needed for reliable markerless gait analysis?

Markerless motion capture is an emerging tool for gait analysis. In some populations, e.g., older adults, traditional gait analysis poses practical challenges, such as reduced assessment tolerance, and the number of strides collected can be limited. This study aimed to investigate the influence of the number of strides on test-retest reliability and measurement error of markerless gait biomechanics in older adults. Twenty strides were extracted from 29 healthy older adults for each of two sessions. Lower-limb kinematics and kinetics were computed. Subsequently, non-consecutive random subsets of k=2-19 strides were averaged within-subjects and within-sessions, including scenarios with unequal k between sessions. Integrated Intraclass Correlation Coefficients (iICC A,k ) and Standard Errors of Measurement (SEM) were calculated for trajectory data. ICC A,k [Confidence Intervals] were computed for the range of motion and peaks. Two arbitrary thresholds for the minimally acceptable number of strides were combined: (1) the smallest k that yielded an ICC within 10% of the maximum ICC across all k, and (2) an absolute ICC threshold of 0.75. SEM≤2º was deemed suitable for kinematics, and SEM%≤5% for kinetics. For joint angles and moments, iICC dropped less than 10% from the highest iICC when using ≥7 strides with equal number of strides across sessions, attaining iICCs≥0.75. Reducing the number of strides in one session had less impact than reducing both equally. Lower Confidence intervals were generally ≥0.75 for discrete parameters. Kinematic SEM rarely exceeds 2º. Globally, 4 strides are needed to have a kinetics SEM%≤5%. A minimum of 7 strides contributing to the average observation is generally sufficient to achieve reliable markerless kinematics and kinetics in older adults. These results have particular relevance to populations who may experience limited tolerance for lengthy assessments. Allowing flexibility in stride number collected across sessions, while maintaining reliability, contributes to optimizing data collection strategies. • Averaging 7 strides provides reliable markerless gait data in older adults • Reliable outcomes are possible despite having fewer strides available in one session • The number of strides collected during older adults’ gait analysis over time can vary

Authors

Institutions

Publication Details

Journal
Gait & Posture
Published
2025-10-22
DOI
https://doi.org/10.1016/j.gaitpost.2025.110020
Citations
1
Primary Topic
Diabetic Foot Ulcer Assessment and Management
Type
article
Field-Weighted Citation Impact
1.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How many strides are needed for reliable markerless gait analysis?

António Veloso, Vera Moniz-Pereira, Todd C. Pataky, Andreia Carvalho et al.
1 citations
Gait & Posture
Diabetic Foot Ulcer Assessment and Management
1.29
article

How many strides are needed for reliable markerless gait analysis?

António Veloso, Vera Moniz-Pereira, Todd C. Pataky, Andreia Carvalho, Mark A. Robinson, Jos Vanrenterghem
article en
1 citations

Abstract

Markerless motion capture is an emerging tool for gait analysis. In some populations, e.g., older adults, traditional gait analysis poses practical challenges, such as reduced assessment tolerance, and the number of strides collected can be limited. This study aimed to investigate the influence of the number of strides on test-retest reliability and measurement error of markerless gait biomechanics in older adults. Twenty strides were extracted from 29 healthy older adults for each of two sessions. Lower-limb kinematics and kinetics were computed. Subsequently, non-consecutive random subsets of k=2-19 strides were averaged within-subjects and within-sessions, including scenarios with unequal k between sessions. Integrated Intraclass Correlation Coefficients (iICC A,k ) and Standard Errors of Measurement (SEM) were calculated for trajectory data. ICC A,k [Confidence Intervals] were computed for the range of motion and peaks. Two arbitrary thresholds for the minimally acceptable number of strides were combined: (1) the smallest k that yielded an ICC within 10% of the maximum ICC across all k, and (2) an absolute ICC threshold of 0.75. SEM≤2º was deemed suitable for kinematics, and SEM%≤5% for kinetics. For joint angles and moments, iICC dropped less than 10% from the highest iICC when using ≥7 strides with equal number of strides across sessions, attaining iICCs≥0.75. Reducing the number of strides in one session had less impact than reducing both equally. Lower Confidence intervals were generally ≥0.75 for discrete parameters. Kinematic SEM rarely exceeds 2º. Globally, 4 strides are needed to have a kinetics SEM%≤5%. A minimum of 7 strides contributing to the average observation is generally sufficient to achieve reliable markerless kinematics and kinetics in older adults. These results have particular relevance to populations who may experience limited tolerance for lengthy assessments. Allowing flexibility in stride number collected across sessions, while maintaining reliability, contributes to optimizing data collection strategies. • Averaging 7 strides provides reliable markerless gait data in older adults • Reliable outcomes are possible despite having fewer strides available in one session • The number of strides collected during older adults’ gait analysis over time can vary

Gait & PostureVol. 123
University of Lisbon (PT), Universidade Politécnica de Lisboa (PT), Kyoto University (JP), Liverpool John Moores University (GB), KU Leuven (BE)
Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 14%
Diabetic Foot Ulcer Assessment and Management
1.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.