Optimized control mapping through user-tuned cost of effort, time, and reliability^.

Area of Science:

• Robotics
• Neuroscience
• Biomechanics

Background:

• Human joint coordination is crucial for task performance.
• Computational motor control theory uses optimal control to explain movement.
• The brain likely combines multiple cost functions with adjustable weights for different tasks.

Purpose of the Study:

• To investigate how varying weights of cost functions impact user control signals and machine output mapping.
• To hypothesize that human-machine interfaces should align with user-prioritized movement costs.
• To apply this framework to prosthesis control tuning.

Main Methods:

• Developed a hierarchical optimization model.
• Independently optimized user control signals and device virtual dynamics.
• Studied the effect of composite movement cost function weights.

Main Results:

• Demonstrated the feasibility of optimizing human-machine interfaces based on user costs.
• Showed how relative cost weights influence optimal control signals.
• Indicated potential for personalized prosthesis tuning.

Conclusions:

• The proposed framework supports tuning prostheses based on user-valued costs.
• This approach allows for customization of prosthetic platforms to individual patient needs.
• Facilitates collaboration between clinicians and users in optimizing device performance.