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

Area of Science:

• Biomechanics
• Computational Biology
• Medical Engineering

Background:

• Degenerative processes, such as repetitive strain injuries (RSIs), alter normal movement patterns over time.
• Tracking these slow, hidden processes is crucial for early intervention but challenging due to complexity and lack of direct measurement.
• Existing mechanical system models for damage accumulation offer a potential alternative for biomechanical analysis.

Purpose of the Study:

• To investigate if algorithms used for tracking mechanical damage accumulation can be applied to monitor hidden biomechanical processes.
• To assess the feasibility of using easily obtainable kinematic data to infer the progression of such biological changes.

Main Methods:

• Five healthy adults walked on a motorized treadmill with a gradually increasing inclination.
• Sagittal plane kinematics of the hip, knee, and ankle were recorded.
• Scalar tracking metrics were derived from kinematic data, with treadmill inclination serving as the "damage" proxy.

Main Results:

• Scalar tracking metrics showed strong cubic relationships with treadmill inclination (r² range: 88.9%–98.2%, p<0.001).
• This indicates a high correlation between measurable kinematic changes and the simulated "damage" (inclination).

Conclusions:

• The proposed algorithmic approach shows promise for tracking and predicting slow-progressing degenerative biological processes like muscle fatigue or RSIs.
• Easily acquired biomechanical data can provide valuable insights into "hidden" biological dynamics that are difficult to measure directly.