Computer methods and programs in biomedicine · 2025
A novel soft tissue-integrated kinematic solver for skeletal motion: Validation and applications.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Duquesne K, Van Oevelen A, Sijbers J, Van Paepegem W, Audenaert E
Аннотация
BACKGROUND AND OBJECTIVE: Kinematic solvers for human motion analysis, relying on oversimplified joint definitions, face inherent limitations in capturing the true spectrum of skeletal motion. Recent advancements incorporated soft tissue constraints to derive more realistic joint kinematics. However, these methods require marker data input and are computationally expensive, limiting their application to specific joints. This paper proposes a novel kinematic solver that addresses this gap by explicitly accounting for soft tissues, while allowing for accurate and computational efficient modeling across diverse movements and joints.
METHODS: The proposed soft tissue-integrated kinematic solver determines the kinematics by relying on the principle of force balance. In a cascaded iterative way, the position and orientation of each individual segment is updated by minimizing the force residual acting on the segment The latter is solved through a unique way by defining and aligning two point clouds. Accuracy was assessed with three datasets: in-vivo MRI squats (N = 9), in-vitro cadaver CT squat (N = 1), and in-vitro cadaver arm flexion/extension/pro-supination (N = 1). The accuracy was assessed by computing the absolute error on the joint angles and translations and benchmarked against traditional inverse kinematics with a revolute joint as well as two computer vision techniques (OSSO and SKEL).
RESULTS: All experiments showed that with sufficient input data (over 5 rigid bone markers, or skin zones), the primary motion error was almost without exception under 1.5° This outperformed the inverse kinematics with revolute joint (7.29° flexion-extension), OSSO (9.59° flexion-extension) and SKEL (3.19° flexion-extension) methods. The median error on the secondary kinematics for the humeroulnar and ulnoradial joints were below 3.78° and 2.50 mm when driving the motion with skin zones. For the tibiofemoral joints, errors were under 5.39° and 3.5 mm. Computation time was below 30 s per frame.
CONCLUSIONS: The kinematic solver enables exploring all degrees of freedom accurately without compromising computational efficiency. Unlike biomechanical methods which are limited to marker data, the kinematic solver can analyze both marker and skin data.
METHODS: The proposed soft tissue-integrated kinematic solver determines the kinematics by relying on the principle of force balance. In a cascaded iterative way, the position and orientation of each individual segment is updated by minimizing the force residual acting on the segment The latter is solved through a unique way by defining and aligning two point clouds. Accuracy was assessed with three datasets: in-vivo MRI squats (N = 9), in-vitro cadaver CT squat (N = 1), and in-vitro cadaver arm flexion/extension/pro-supination (N = 1). The accuracy was assessed by computing the absolute error on the joint angles and translations and benchmarked against traditional inverse kinematics with a revolute joint as well as two computer vision techniques (OSSO and SKEL).
RESULTS: All experiments showed that with sufficient input data (over 5 rigid bone markers, or skin zones), the primary motion error was almost without exception under 1.5° This outperformed the inverse kinematics with revolute joint (7.29° flexion-extension), OSSO (9.59° flexion-extension) and SKEL (3.19° flexion-extension) methods. The median error on the secondary kinematics for the humeroulnar and ulnoradial joints were below 3.78° and 2.50 mm when driving the motion with skin zones. For the tibiofemoral joints, errors were under 5.39° and 3.5 mm. Computation time was below 30 s per frame.
CONCLUSIONS: The kinematic solver enables exploring all degrees of freedom accurately without compromising computational efficiency. Unlike biomechanical methods which are limited to marker data, the kinematic solver can analyze both marker and skin data.