Generating Individual Gait Kinetic Patterns Using Machine Learning

Bouças, César; Ferreira, José Ribeiro; Coimbra, A. Paulo; Crisóstomo, Manuel; Mendes, P. Amado

doi:10.1007/978-3-030-42520-3_5

Cited by 1 publication

(3 citation statements)

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“…Even though intra-subject variability was minimized by CR, no obvious patterns could be found here to link between bone morphometry and observable patterns in motion tasks. Alternatively, prediction performance may improve using deep learning methodology, however, such would require sample sizes to be substantially forced up ( Bouças et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Given the profound dominance of size in statistical shape models ( Audenaert et al, 2019a ), CCA was applied on the first PC weight vector from the kinematic model and the PC weights of the shape samples. Additionally, the correlation between a set of general demographic characteristics (i.e., age, length, and weight) of our test subjects and the main kinematic mode was established, similar to the gait prediction studies of Bouças et al (2019) and Moissenet et al (2019) . Correlations were tested for significance by means of the Wilks’ lambda likelihood ratio statistic (α = 0.05).…”

Section: Methodsmentioning

confidence: 96%

“…As such, simulation output denoting subject immobility was rejected ( Van Houcke et al, 2019 ; De Roeck et al, 2020 ). Then, each kinematic curve was discretized into 101 registration entries assigning 0–100% of movement progression ( Sadeghi et al, 2003 ; Schwartz et al, 2004 ; Chau et al, 2005 ; Deluzio and Astephen, 2007 ; Kobayashi et al, 2016 ; Matsuki et al, 2017 ; Bouças et al, 2019 ; Moissenet et al, 2019 ; Van Houcke et al, 2019 ; De Roeck et al, 2020 ; Reznick et al, 2020 ; Duquesne et al, 2021 ; Warmenhoven et al, 2021 ). At last, a continuous registration (CR) method was applied to remove the phase variability of the curves ( Sadeghi et al, 2003 ; Chau et al, 2005 ; Duquesne et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Roeck

Duquesne

Houcke

et al. 2021

Front. Bioeng. Biotechnol.

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Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics.Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated.Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°.Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.

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Section: Discussionmentioning

confidence: 99%