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Background <p>Functional leg axis deviations, particularly dynamic knee valgus (DKV), are considered key risk factors for lower-extremity injuries. In physiotherapy practice there are no standardized tools to quantitatively assess functional leg axis deviations within a treatment session. While application-based 2D analyses offer accessible options, the agreement between manual and automated, AI-based Human Pose Estimation (HPE) methods remains underexplored. Here, the aims were to investigate the intra- and inter-rater reliability of a manual application-based method (PhysioMaster®); to quantify the agreement between two AI-based HPE models (OpenPose, BlazePose); and to explore which functional test, Single-Leg Squat (SLS), Single-Leg Hop for Distance (SLH), or Single-Leg Landing (SLL), elicits the most pronounced leg axis deviations.</p> Methods <p>Sixteen healthy adults (8 females) performed three standardized single-leg tasks (SLS, SLH, SLL) with each leg. Knee angles in the frontal plane were measured from monocular video images using the PhysioMaster® application (two raters × two ratings) and compared with automated 2D analyses using OpenPose and BlazePose. Reliability and agreement were evaluated using Intraclass Correlation Coefficients, Lin’s Concordance Correlation Coefficient (CCC), and Bland–Altman analyses. Linear Mixed Model (LMM) analyses were carried out to explore which functional task would be the most effective.</p> Results <p>The PhysioMaster® application showed excellent intra- and inter-rater reliability across all tests (ICC ≥ 0.95). OpenPose demonstrated excellent agreement with manual application-based measurements (CCC = 0.95; Bias = 0.87°; LoA = –4.32° to 6.05°), while BlazePose achieved good agreement (CCC = 0.89; Bias = –0.67°; LoA = –8.76° to 7.42°). LMM analyses revealed that all three tasks significantly evoked knee angle deviations. The SLL evoked the largest valgus angles (Estimate = –8.29°, <i>p</i> &lt; .001), while the SLS elicited the highest varus angles (Estimate = 7.92°, <i>p</i> &lt; .001). </p> Conclusion <p>Both, manual application-based and AI-based 2D methods, provided reliable and largely consistent assessments of knee valgus angles in the frontal plane during functional testing. OpenPose achieved the best agreement with manual application-based reference values, and the SLL was the most suitable task for detecting DKV. While the results support the integration of manual application-based and AI-assisted posture analysis into clinical physiotherapy, further research on concurrent validity of all three methods used against 3D motion capture is needed. </p>

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Assessment of dynamic knee angle deviations in the frontal plane in physiotherapy clinical practice: intra- and inter-rater reliability of an application and agreement with two AI-models

  • 期刊论文

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  • 发表日期：

    2026年5月12日

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  • Lukas Janisch,
  • Lilo-Marie Maack,
  • Dominik Fohrmann,
  • Carlos J. Marques

摘要

Background

Functional leg axis deviations, particularly dynamic knee valgus (DKV), are considered key risk factors for lower-extremity injuries. In physiotherapy practice there are no standardized tools to quantitatively assess functional leg axis deviations within a treatment session. While application-based 2D analyses offer accessible options, the agreement between manual and automated, AI-based Human Pose Estimation (HPE) methods remains underexplored. Here, the aims were to investigate the intra- and inter-rater reliability of a manual application-based method (PhysioMaster®); to quantify the agreement between two AI-based HPE models (OpenPose, BlazePose); and to explore which functional test, Single-Leg Squat (SLS), Single-Leg Hop for Distance (SLH), or Single-Leg Landing (SLL), elicits the most pronounced leg axis deviations.

Methods

Sixteen healthy adults (8 females) performed three standardized single-leg tasks (SLS, SLH, SLL) with each leg. Knee angles in the frontal plane were measured from monocular video images using the PhysioMaster® application (two raters × two ratings) and compared with automated 2D analyses using OpenPose and BlazePose. Reliability and agreement were evaluated using Intraclass Correlation Coefficients, Lin’s Concordance Correlation Coefficient (CCC), and Bland–Altman analyses. Linear Mixed Model (LMM) analyses were carried out to explore which functional task would be the most effective.

Results

The PhysioMaster® application showed excellent intra- and inter-rater reliability across all tests (ICC ≥ 0.95). OpenPose demonstrated excellent agreement with manual application-based measurements (CCC = 0.95; Bias = 0.87°; LoA = –4.32° to 6.05°), while BlazePose achieved good agreement (CCC = 0.89; Bias = –0.67°; LoA = –8.76° to 7.42°). LMM analyses revealed that all three tasks significantly evoked knee angle deviations. The SLL evoked the largest valgus angles (Estimate = –8.29°, p < .001), while the SLS elicited the highest varus angles (Estimate = 7.92°, p < .001).

Conclusion

Both, manual application-based and AI-based 2D methods, provided reliable and largely consistent assessments of knee valgus angles in the frontal plane during functional testing. OpenPose achieved the best agreement with manual application-based reference values, and the SLL was the most suitable task for detecting DKV. While the results support the integration of manual application-based and AI-assisted posture analysis into clinical physiotherapy, further research on concurrent validity of all three methods used against 3D motion capture is needed.