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Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Wearable Technology

Background:

  • Ground reaction forces (GRF) during load carriage differ from unloaded walking.
  • Quantifying peak vertical GRF (pGRFvert) in operational settings is crucial for understanding Soldier biomechanics.
  • Existing methods often require laboratory equipment, limiting real-world application.

Purpose of the Study:

  • To develop a statistically based model for predicting pGRFvert during loaded walking.
  • To utilize data from the ActiGraph GT3X+ activity monitor (AM) vertical acceleration for this prediction.

Main Methods:

  • Fifteen male Soldiers walked on an instrumented treadmill with varying loads (0-46 kg) and footwear.
  • Data from an ActiGraph GT3X+ AM (vertical acceleration) and a force plate were collected.
  • A regression equation was developed using average peak vertical AM acceleration (pACCvert) and pGRFvert, validated with a leave-one-subject-out approach.

Main Results:

  • The predictive model incorporated pACCvert, body mass, carried load mass, and their interaction.
  • Cross-validation yielded an average absolute percent difference (AAPD) of 4.0% ± 2.7% and RMSE of 69.5 N.
  • Validation on novel datasets showed an AAPD of 5.5% ± 3.9% and RMSE of 78.7 N.

Conclusions:

  • A statistically derived equation accurately predicts pGRFvert in Soldiers carrying loads during walking.
  • This model enables GRF estimation using wearable AMs, eliminating the need for force plates in operational research.