Validity and Reliability of 2-Dimensional Video-Based Assessment to Analyze Foot Strike Pattern and Step Rate During Running: A Systematic Review

Oliveira, Fábio Carlos Lucas de; Fredette, Anny; Echeverría, Sherezada Ochoa; Batcho, Charles Sèbiyo; Roy, Jean-Sébastien

doi:10.1177/1941738119844795

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…In addition, methods such as the visual analysis performed by a clinician of a simple 2D video [ 21 ], recorded via normal commercial/mobile phone cameras, further reduce costs. Unfortunately, the latter methodology is burdened by subjectivity (e.g., reliance on the experience of the clinician performing the video analysis), low external validity (e.g., the influence of the setup), and understudied overall validity [ 16 , 20 ]. Compared to the above solutions, Graal offers an automatic, non-operator-dependent system that provides robust performance independent from the absolute ambient light conditions (though it requires a stable, artificial light source) [ 35 ] and the camera’s sampling rate (though it requires a minimum of 30 Hz and a minimum of 2048 frames) and that can be used on videos with a low pixel resolution.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the several advantages compared to gold-standard methods, these systems require setup/subject preparation, calibration, and data post-processing procedures that reduce the ease of use. In recent years, no-calibration and marker-less video analysis methods have been developed, but their accuracy and precision remain to be assessed by validation studies [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

Simoni

Scarton

Macchi

et al. 2021

Sensors

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Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait—the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel’s brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland–Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r2 = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r2 = 0.98). Bland–Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r2 = 0.00038; 0.21 ± 0.96, p = 0.87, r2 = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

Simoni

Scarton

Macchi

et al. 2021

Sensors

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“…Our developed method that automates LESS scores provides a viable solution to decreasing scoring time, increasing accessibility to non-expert raters, and delivering immediate results without any additional expenditure other than conventional video recordings. Conventional 2D video recordings are adequate for quantifying kinematics [32][33][34] and are readily accessible through tablets or smartphones. The successful application of this method would pave the way for the automatic detection of individuals at high risk of injury using smartphone-based applications of LESS and 2D video footage (Video S1: https://youtu.be/q1wiGt4K8MU).…”

Section: Discussionmentioning

confidence: 99%

The ‘DEEP’ Landing Error Scoring System

et al. 2020

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The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.

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“…This study focused on the foot strike pattern analysis; thereafter, a 2D video analysis in the sagittal plane was chosen for the overground running measures. Those variables, including contact time, flight time, stride frequency, and stride length, which might provide more insight into running strike analysis, were not collected or analyzed due to equipment limitations or lack of understanding of the reliability of using 2D video analyses [ 23 , 35 , 36 ]. While running-related overuse injuries have been considered as an important issue, knowledge of the foot strike pattern would appear to be important information for the clinician managing a runner with a running-related overuse injury.…”

Section: Discussionmentioning

confidence: 99%

Will the Foot Strike Pattern Change at Different Running Speeds with or without Wearing Shoes?

Lai

Chou

Chu

et al. 2020

IJERPH

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Runners strike their feet with three different patterns during running: forefoot, midfoot, and rearfoot. This study aimed to investigate whether runners maintain consistent patterns while running speed and foot condition change. The foot strike patterns of runners when running on a treadmill at paces ranging from slow to fast were recorded from twenty healthy male regular runners, with and without shoes, in random order. A high-speed camera was used to observe the strike patterns, which were then categorized by an experienced physical therapist. Linear-log and Pearson chi-square analysis with a significance level of α = 0.05 was performed to examine the correlation between foot strike pattern, running speed, and shoe conditions. The results suggest that runners strike with different patterns when running with and without shoes (χ2 = 99.07, p < 0.01); runners preferred to adopt heel strike regardless of running speeds when running with shoes. While running barefoot, only 23.8% of landing strikes were rearfoot, and the strike pattern distribution did not change significantly with the running speed (χ2 = 2.26, p = 0.89). In summary, the foot strike preference of runners is correlated with the foot condition (barefoot or shod) rather than running speed. For runners who intend to change their strike patterns for any reason, we recommend that they consider adjusting their footwear, which may naturally help with the foot strike adjustment. Future studies should attempt to use advanced techniques to observe further foot biomechanics in order to discover if changing strike pattern is directly correlated with lower limb injuries.

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Validity and Reliability of 2-Dimensional Video-Based Assessment to Analyze Foot Strike Pattern and Step Rate During Running: A Systematic Review

Cited by 18 publications

References 37 publications

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

The ‘DEEP’ Landing Error Scoring System

Will the Foot Strike Pattern Change at Different Running Speeds with or without Wearing Shoes?

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