A robust ensemble data method for identification of human joint mechanical properties during movement.

Area of Science:

• Control Systems Engineering
• Biomedical Engineering
• System Identification

Background:

• Accurate identification of linear time-varying systems is crucial for understanding complex dynamics.
• Unknown inputs, such as voluntary joint movements, pose significant challenges in system identification.
• Existing methods often struggle with intertrial variations and tracking dynamic changes.

Purpose of the Study:

• To develop a robust perturbation method for identifying linear time-varying systems with unknown voluntary joint input.
• To separate unknown voluntary input from system perturbation using high-pass filtering.
• To transform multi-input single-output identification into single-input single-output identification.

Main Methods:

• A novel perturbation method utilizing ensemble data.
• High-pass filtering to distinguish between unknown input and perturbation signals.
• Recasting multi-input single-output (MISO) system identification into single-input single-output (SISO) problems.
• Analysis and simulations tailored to human arm dynamics.

Main Results:

• The proposed method demonstrates robustness to intertrial variations.
• The system can track changes in system dynamics up to 5 Hz.
• Simulations validate the method under conditions similar to human arm experiments.
• Experimental results reveal that human elbow joint mechanical properties vary with voluntary movement speed.

Conclusions:

• The developed perturbation method is effective for identifying linear time-varying systems with unknown inputs.
• Mechanical properties of the human elbow joint are influenced by voluntary movement speed.
• The identified mean stiffness during voluntary movement is comparable to postural stiffness but higher than previously reported values.