A nonlinear approach to tracking slow-time-scale changes in movement kinematics

Abstract

Degenerative processes like repetitive strain injuries (RSIs) cause normal movement patterns to change slowly over time. Accurately tracking how these disease/injury processes evolve over time and predicting their future progression could allow early intervention and prevent further deterioration. However, these processes often cannot be measured directly and first-principles models of these processes and how they affect movement control are highly complex and difficult to derive analytically. This study was conducted to determine if algorithms developed to track damage accumulation in mechanical systems without requiring first-principles models or direct measurements of the damage itself could also track a similar “hidden” process in a biomechanical context. Five healthy adults walked on a motorized treadmill at their preferred speed, while the treadmill inclination angle was slowly increased from 0° (level) to approximately +8°. Sagittal plane kinematics for the left hip, knee, and ankle joints were computed. The treadmill inclination angle was independently recorded and defined the “damage” to be tracked. Scalar tracking metrics were computed from the lower extremity walking kinematics. These metrics exhibited strong cubic relationships with treadmill inclination (88.9%⩽ r 2⩽98.2%; p<0.001). These results demonstrate that the proposed approach may also be well suited to tracking and predicting slow-time-scale degenerative biological processes like muscle fatigue or RSIs. This possibility is potentially quite powerful because it suggests that easily obtainable biomechanical data can provide unique and valuable insights into the dynamics of “hidden” biological processes that cannot be easily measured themselves.