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Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming.

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Network Medicine

Background:

  • Traditional gait analysis often overlooks the interconnected movements of the entire body.
  • Human walking involves a complex interplay between limbs, trunk, and other body segments.
  • A body sensor network (BSN) can capture these distributed signals.

Purpose of the Study:

  • To propose and validate a body sensor network (BSN) model for human locomotion.
  • To investigate if accelerometer data can effectively represent the interconnectedness of body nodes during walking.
  • To explore the application of machine learning in analyzing complex gait patterns.

Main Methods:

  • Collected accelerometer data from six body locations in ten healthy participants.
  • Utilized genetic programming, a machine learning technique, to develop non-linear symbolic models.
  • Hypothesized that accelerometer data could model the BSN of human locomotion.

Main Results:

  • The developed BSN models best described subject-specific walking data when using the lower back accelerometer.
  • Models demonstrated effectiveness in capturing individual gait characteristics.
  • Inter-subject variability in body size impacted model generalization across participants.

Conclusions:

  • A BSN approach reveals essential network-medicine relationships between body nodes for accurate gait description.
  • Subject-specific BSN models are crucial for understanding individual walking patterns.
  • Findings have implications for precision medicine, clinical diagnostics, and establishing healthy gait baselines.