Predicting normative walking biomechanics across the lifespan using seven simple features

Area of Science:

• Biomechanics
• Human Movement Science
• Orthopedics

Background:

• Gait impairment assessment requires normative data for comparison.
• Fair assessment necessitates controlling for factors like sex, anthropometry, and walking characteristics.
• Existing normative data may not cover the full lifespan or specific kinematic/kinetic parameters.

Purpose of the Study:

• To develop statistical models predicting lower-limb kinematics and kinetics during walking.
• To establish normative reference data for healthy gait across the lifespan (ages 3-91).
• To utilize simple covariates for model development, enhancing clinical applicability.

Main Methods:

• Developed 16 statistical models predicting 16 joint kinematics and kinetics.
• Utilized covariates: sex, age, height, mass, side, walking speed, and cadence.
• Data collected from 301 healthy participants aged 3 to 91 years.

Main Results:

• Models accurately predicted joint angles, ground reaction forces, joint moments, and joint powers.
• Root mean squared error (RMSE) values indicate good model performance across parameters.
• Provided uncertainty values for generated normative data.

Conclusions:

• The developed models offer reliable normative data for gait analysis.
• Accessible online and local apps facilitate the use of these models by clinicians and scientists.
• This work supports objective movement impairment analysis and clinical decision-making.