Mechanical loading prediction through accelerometry data during walking and running.

Area of Science:

• Biomechanics
• Sports Science
• Wearable Technology

Background:

• Assessing mechanical loading, including peak ground reaction forces (pGRF) and peak loading rate (pLR), is crucial in clinical settings but currently lacks practical methods.
• Existing methods for measuring these variables are often confined to laboratory environments, limiting their applicability in free-living conditions.

Purpose of the Study:

• To develop and validate accelerometry-based regression equations for predicting pGRF and pLR during walking and running.
• To provide a non-invasive and accessible tool for estimating key biomechanical loading parameters.

Main Methods:

• One hundred thirty-one participants (79 females) walked and ran on a force plate-instrumented treadmill at speeds ranging from 2-14 km·h⁻¹.
• Accelerometers were worn at the ankle, lower back, and hip, with data used to develop prediction equations for pGRF and pLR.
• Leave-one-out cross-validation and Bland-Altman plots were employed to assess prediction accuracy, with comparisons made to existing reference equations.

Main Results:

• Predictive equations for pGRF demonstrated high accuracy, with coefficients of determination above 0.96 and a mean absolute percent error (MAPE) below 7.3%.
• The developed pGRF equations outperformed previously published reference equations.
• Equations for predicting pLR showed lower accuracy compared to those for pGRF, though still provided valuable estimations.

Conclusions:

• Accelerometry-based equations offer a highly accurate and practical method for predicting pGRF during walking and running.
• These findings enable easier assessment of mechanical loading in free-living environments.
• While pLR prediction accuracy is moderate, accelerometry provides a promising avenue for future research and clinical application.