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Related Experiment Video

Updated: Jun 29, 2026

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

Muscle forces during running predicted by gradient-based and random search static optimisation algorithms.

Ross H Miller¹, Jason C Gillette, Timothy R Derrick

¹Department of Kinesiology, University of Massachusetts, Amherst, MA, USA. rhmiller@kin.umass.edu

Computer Methods in Biomechanics and Biomedical Engineering

|October 2, 2008

Summary

Random search algorithms offer a more effective approach to predicting muscle forces during running compared to Sequential Quadratic Programming (SQP). These methods yield more accurate estimations by better locating global minima in complex optimization problems.

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Last Updated: Jun 29, 2026

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running

Published on: July 17, 2020

Area of Science:

Biomechanics
Human Movement Science
Computational Biology

Background:

Muscle force prediction is crucial for understanding locomotion.
Sequential Quadratic Programming (SQP) is commonly used but has limitations.
SQP may overestimate forces and underestimate co-contraction due to optimization challenges.

Purpose of the Study:

To compare the effectiveness of SQP and random search (RS) algorithms for muscle force prediction.
To investigate if RS algorithms improve the accuracy of static optimization in biomechanics.
To evaluate muscle force predictions during the stance phase of running.

Main Methods:

Muscle forces for lower extremity muscles were predicted in 10 subjects during running.
Static optimization was performed using SQP and two RS algorithms (genetic algorithm, simulated annealing).
The cost function minimized was the sum of cubed muscle stresses.

Main Results:

RS algorithms predicted significantly smaller peak forces (42% less on average) and muscle impulses (46% less on average) than SQP.
RS algorithms consistently found solutions with lower cost function scores.
This indicates RS algorithms are better at finding the global minimum for this optimization problem.

Conclusions:

Random search algorithms appear more effective than SQP for static optimization of muscle forces.
RS algorithms may provide more realistic estimations of muscle forces during locomotion.
These findings suggest a potential improvement in biomechanical modeling accuracy.