AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through...

Area of Science:

• Biomechanics
• Human Motion Analysis
• Musculoskeletal Modeling

Background:

• Manual processing of motion capture data is time-consuming and expensive.
• This limits the creation and sharing of large-scale human biomechanics datasets.
• Automating this process is crucial for advancing research in human movement and related fields.

Approach:

• Developed AddBiomechanics, an automated method using linear and non-convex optimization.
• Scales musculoskeletal models, registers markers, and computes kinematics.
• Determines segment masses and refines kinematics for dynamic consistency.

Key Points:

• Reduces processing time from ~1 day to <30 minutes.
• Achieves close correspondence with expert calculations and low error metrics.
• Validated with existing datasets and synthetic walking data.

Conclusions:

• AddBiomechanics offers an automated, standardized, and efficient solution for human movement dynamics quantification.
• The open-source platform lowers barriers to entry for biomechanical analysis, promoting data sharing and collaborative research.
• Widespread adoption can accelerate breakthroughs in understanding human motion, diseases, and assistive technologies.