Predictive Simulations with OpenSim Moco to Investigate the Interaction Between Human and Assistive Exoskeleton.

Area of Science:

• Biomechanics
• Robotics
• Human-Computer Interaction

Background:

• Lower limb exoskeletons are increasingly used to assist human movement.
• Understanding the human-exoskeleton interaction is crucial for optimizing assistance.
• Previous research has explored exoskeleton assistance, but detailed simulation of varying assistance levels is needed.

Purpose of the Study:

• To investigate the interaction between humans and lower limb assistive exoskeletons.
• To quantify the effect of different assistance levels on human effort and biomechanics.
• To utilize computational simulations for analyzing human-exoskeleton dynamics.

Main Methods:

• A human-exoskeleton interaction model was developed.
• Predictive simulations were performed using OpenSim Moco software.
• Root Mean Square (RMS) torque and muscle activations were analyzed across varying assistance levels.

Main Results:

• Increased exoskeleton assistance significantly reduced human physical effort.
• Robot assistance led to decreased human RMS torque and muscle activation.
• The study successfully simulated and quantified the impact of exoskeleton assistance levels.

Conclusions:

• Higher levels of robotic assistance in lower limb exoskeletons effectively reduce the physical burden on users.
• Computational modeling provides valuable insights into human-exoskeleton dynamics and can guide exoskeleton design.
• Future work will explore more complex movements and patient-specific models, such as those with hemiparesis.