CPU Optimization of a Monocular 3D Biomechanics Pipeline for Low-Resource Deployment

TL;DR

This paper enhances a monocular 3D biomechanics pipeline for CPU-only systems, achieving significant speedups and maintaining high accuracy, enabling low-resource deployment in clinical and sports environments.

Contribution

It introduces system-level optimizations that enable research-grade biomechanics analysis to run efficiently on consumer CPUs without GPU reliance.

Findings

2.47x increase in processing throughput

59.6% reduction in total runtime

High biomechanical output consistency (mean joint-angle deviation 0.35°)

Abstract

Markerless 3D movement analysis from monocular video enables accessible biomechanical assessment in clinical and sports settings. However, most research-grade pipelines rely on GPU acceleration, limiting deployment on consumer-grade hardware and in low-resource environments. In this work, we optimize a monocular 3D biomechanics pipeline derived from the MonocularBiomechanics framework for efficient CPU-only execution. Through profiling-driven system optimization, including model initialization restructuring, elimination of disk I/O serialization, and improved CPU parallelization. Experiments on a consumer workstation (AMD Ryzen 7 9700X CPU) show a 2.47x increase in processing throughput and a 59.6\% reduction in total runtime, with initialization latency reduced by 4.6x. Despite these changes, biomechanical outputs remain highly consistent with the baseline implementation (mean joint-angle deviation 0.35$^\circ$, $r=0.998$). These results demonstrate that research-grade vision-based biomechanics pipelines can be deployed on commodity CPU hardware for scalable movement assessment.