A novel soft tissue-integrated kinematic solver for skeletal motion: Validation and applications

Area of Science:

• Biomechanics
• Human Motion Analysis
• Computational Kinematics

Background:

• Traditional kinematic solvers use simplified joint models, limiting skeletal motion accuracy.
• Advanced methods incorporating soft tissue are computationally expensive and marker-dependent.
• A gap exists for efficient, accurate kinematic modeling across various joints and movements.

Purpose of the Study:

• To develop a novel kinematic solver that explicitly accounts for soft tissues.
• To enable accurate and computationally efficient human motion analysis.
• To model diverse movements and joints beyond marker-based limitations.

Main Methods:

• A force balance principle drives the soft tissue-integrated kinematic solver.
• Segment kinematics are iteratively updated by minimizing force residuals using point cloud alignment.
• Accuracy validated on in-vivo MRI and in-vitro cadaver datasets (squats, arm movements).

Main Results:

• Primary motion error consistently below 1.5° with sufficient marker or skin data.
• Significantly outperformed traditional inverse kinematics and computer vision techniques.
• Secondary kinematics showed median errors below 3.78° (humeroulnar) and 5.39° (tibiofemoral).
• Computation time remained under 30 seconds per frame.

Conclusions:

• The solver accurately captures all degrees of freedom efficiently.
• It analyzes both marker and skin data, overcoming limitations of marker-only biomechanical methods.
• Enables broader application of precise human motion analysis.