Multiphase and Multivariable Linear Controllers That Account for the Joint Torques in Normal Human Walking.

Area of Science:

• Biomechanics and Robotics
• Human Movement Analysis
• Control Systems Engineering

Background:

• Understanding the complex interplay of joint torques, angles, and velocities during human locomotion is crucial for developing effective assistive and augmentation devices.
• Previous research has focused on single-joint or single-phase control models, which may not fully capture the dynamic, multi-joint nature of walking.

Purpose of the Study:

• To determine if a limited set of sequentially composed multivariable linear controllers can effectively model the defining relationships within human walking data.
• To investigate the potential for these controllers to generalize across multiple subjects and phases of the gait cycle.

Main Methods:

• A mixed integer programming problem was formulated to identify optimal multivariable and multiphase relationships for hip, knee, and ankle joint parameters.
• The proposed controller framework was applied to walking data from seven healthy human subjects.

Main Results:

• Four sequentially composed, independently activated multivariable linear controllers accurately represented the joint torque variations, explaining a significant portion of the variance ([Formula: see text]) across subjects.
• The controller model demonstrated strong generalization capabilities, explaining [Formula: see text] of the variance for new subjects.
• Each of the four controllers was distinctly associated with a specific phase of the gait cycle (e.g., stance, swing), demarcated by key events like toe-off and heel-strike.

Conclusions:

• The developed multiphase multivariable controller significantly improves upon prior single-phase or single-variable models in explaining and generalizing human walking data.
• These findings support the extension of decoupled single-joint control strategies to coupled multijoint multivariable controllers for advanced human assistive and augmentation technologies.