Load-bearing optimization for customized exoskeleton design based on kinematic gait reconstruction.

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Medical Imaging

Background:

• Traditional gait analysis methods are often unsuitable for individuals with acute joint injuries.
• Accurate kinematic data is crucial for designing effective, personalized assistive devices like exoskeletons.

Purpose of the Study:

• To develop a load-bearing optimization method for customized exoskeleton design.
• To enable exoskeleton design using medical imaging data for patients unable to provide traditional gait data.

Main Methods:

• 3D reconstruction of injured joints from CT/MRI scans.
• Surface topography matching and semi-definite computing for kinematic gait reconstruction (KGR).
• Finite element analysis (FEA) using Hertz contact theory to validate gait reconstruction and optimize exoskeleton parameters.

Main Results:

• Successfully reconstructed continuous kinematic skeletal flexion postures from medical images.
• Demonstrated optimization of load-bearing parameters for lower limb exoskeletons based on individual gaits.
• Validated the effectiveness of the KGR method for exoskeleton design.

Conclusions:

• The proposed KGR method provides a viable paradigm for optimizing exoskeleton load-bearing capacity.
• Customized, ergonomic exoskeletons can be designed from medical images, improving suitability for the rehabilitation population.
• This approach offers a pathway for personalized assistive device design in clinical settings.