Bilzon, James

Colyer, Steffi

Cosker, Darren

Evans, Murray

McGuigan, Polly

Needham, Laurie

Wade, Logan

English

OPUS

        Citation for published version:Wade, L, Needham, L, Evans, M, Colyer, S, Cosker, D, Bilzon, J & McGuigan, P 2022, 'Application of deeplearning-based pose estimation methods for clinical gait outcome measures', Congress of the InternationalSociety of Biomechanics, 25/07/21 - 29/07/21.Publication date:2022Document VersionPublisher's PDF, also known as Version of recordLink to publicationUniversity of BathAlternative formatsIf you require this document in an alternative format, please contact:openaccess@bath.ac.ukGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 08. Jun. 2022Application of deep learning-based pose estimation methods for clinical gait outcome measuresLogan Wade, Laurie Needham, Murray Evans, Steffi Colyer, Darren Cosker, James Bilzon, Polly McGuiganCentre for Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdomemail: lw2175@bath.ac.uk•Numerous open-source pose estimation algorithms have been developed, but to date nocomparison has been made between algorithms to examine their accuracy compared tomarker-based motion capture•This study aims to compare clinical gait analysis measures (specifically those related to kneeosteoarthritis), to examine if gait analysis performed using open-source markerless methodscould be employed for clinical gait applications.•AP produced the best agreement to marker- based methods across mostvariables, followed by OP and DLC.•Spatial measures obtained from OP and AP had a very small average bias overone stride (Fig. 1), with an R2 value of 0.99 for step length and velocity, and anR2 value above 0.90 for step width. Therefore, spatial measures, especiallysagittal plane spatial measures, may be sufficiently accurate for clinicalapplications.•AP and OP average biases in the sagittal plane were lower compared to thefrontal plane (Fig. 2), which was evident in the hip frontal plane that wassystematically offset across the whole stride for all methods (Fig. 2B).Systematic offset at the hip was likely due to erroneous manual identification ofhip joint centre locations within the training data of the markerless algorithms.•Planar joint angle variability (SD of bias) was at best, 3.5 ° in the sagittal planeand 2.2 ° in the frontal plane. Average range of motion in the frontal plane forthe maker-based method was 10 ° for the knee and 6 ° for the hip. Thus, jointangle errors are likely too large to detect small meaningful changes during gaitin many clinical conditions, especially in the frontal plane.1.  The spatial measures produced by AP and OP may be sufficiently accurate to detect changes in clinical gait.2.  Joint angle variability in the sagittal plane and variability and accuracy in the frontal plane are currently too large for most clinical applications. [1] Cao, et al. (2019). IEEE Trans. 43(1), 172-186. [2] Fang, et al. (2017). In ICCV (pp. 2334-2343).Fig. 1 (Above): Mean (± SD) differences of step length, step width and centre of mass velocity for each of the three markerless methods compared to marker-based motion capture. R2 values are reported above each variable.Fig. 2 (Right): Mean (± SD) 2D planar joint angles of the knee (A and C) and hip (B and D) in the frontal (A and B) and sagittal (C and D) plane. Timeseries data is normalised to one stride, beginning at heel-strike. Average R2 values over one stride are reported for each method.Acknowledgments:  This investigation was funded by CAMERA, the RCUK Centre for the Analysis of Motion, Entertainment Research and Applications, EP/M023281/1• Fourteen healthy participants performed over-ground constant speed walking while motion capture was obtained from 15 Qualisys camerasand 9 machine-vision cameras at 200 Hz.• Image data from each machine-vision camera were processed using OpenPose[1] (OP), AlphaPose[2] (AP) and the DeepLabCut[3] (DLC) pre-trained human pose model. Joint centre locations were reconstructed in the 3D space using our previously published fusion algorithm[4]• Right ankle, knee, hip and shoulder joint-centre locations were used to calculate step length, step width, centre of mass velocity and planar hipand knee joint angles over one stride.• Outcome measures were compared using Bland-Altman and correlation analysis between marker-based and markerless methods.0 50 100-20-1001020Stride (%)Knee Frontal (Degrees) AbductionAdductionR2: OP = 0.72 AP = 0.78 DLC = 0.41Extension0 50 100-20020406080Stride (%)Knee Sagittal (Degrees) FlexionR2: OP = 0.97 AP = 0.96 DLC = 0.86AdductionAbduction0 50 100-505101520Stride (%)Hip Frontal (Degrees)R2: OP = 0.50 AP = 0.51 DLC = 0.35ExtensionFlexion0 50 100-40-2002040Stride (%)Hip Sagittal (Degrees)R2: OP = 0.95 AP = 0.93 DLC = 0.91Markers OP AP DLCACBDConclusion[3] Mathis, et al. (2018). Nat neurosci, 21(9), 1281-1289. [4] Needham, et al. (2021). Sensors, 21(8), 28893. Retraining pose estimation algorithms with biomechanically accurate training data may be necessary to obtain accurate joint outcome measures.4.  Researchers and clinicians must consider the desired outcome measure and accuracy required for their specific application before implementing these markerless methods in their current form.Introduction MethodsResults & DiscussionStep Length Step Width Velocity-100-50050-100-50050Distance  (mm) Velocity (mm/s)0.99 0.990.690.90 0.940.600.990.990.99OPAPDLC

Application of deep learning-based pose estimation methods for clinical gait outcome measures

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Application of deep learning-based pose estimation methods for clinical gait outcome measures

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