Contribution of Phase Resetting to Statistical Persistence in Stride Intervals: A Modeling Study

Area of Science:

• Biomechanics
• Neuroscience
• Robotics

Background:

• Human walking exhibits statistical persistence in stride intervals, a property affected by aging and neurological conditions.
• The central nervous system (CNS) and biomechanical interactions are hypothesized to generate this persistence.
• Previous complex models suggested phase resetting contributes, but mechanisms remained unclear.

Purpose of the Study:

• To investigate the essential mechanisms underlying statistical persistence in human walking stride intervals.
• To reproduce stride interval persistence using a simplified neuromechanical model.
• To clarify the role of phase resetting in this phenomenon.

Main Methods:

• Developed a simplified neuromechanical model combining a compass-type biomechanical model with a central pattern generator (CPG).
• The CPG model incorporated only phase resetting and a feedforward controller.
• Analyzed the model's phase response characteristics to understand persistence mechanisms.

Main Results:

• The simplified model successfully reproduced statistical persistence in stride intervals.
• Abolishing phase resetting in the model led to a loss of statistical persistence.
• This loss mirrored changes observed in aging, neural disorders, and interventions.

Conclusions:

• Phase resetting within the CPG is a key mechanism for statistical persistence in human walking stride intervals.
• Simplified neuromechanical models can elucidate complex gait control mechanisms.
• Findings offer insights into gait variability and its alterations in various conditions.