🤖 AI Summary
This work addresses the challenge of deploying monocular 3D biomechanical analysis on low-resource CPU-only devices, where existing approaches heavily rely on GPU acceleration. The authors present a CPU-optimized implementation of the MonocularBiomechanics framework through performance-profiling-driven system redesign. Key optimizations include eliminating disk I/O serialization bottlenecks, re-engineering model initialization, and introducing multithreaded parallelism. This is the first demonstration of research-grade, markerless 3D motion analysis operating efficiently in a pure CPU environment. Evaluated on an AMD Ryzen 7 9700X, the optimized pipeline achieves a 2.47× throughput improvement, reduces total runtime by 59.6%, and decreases initialization latency by 4.6×, while maintaining high accuracy—joint angle deviations average only 0.35° (r = 0.998)—thus substantially lowering hardware requirements without compromising analytical precision.
📝 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.