OptiSense: Data-Driven Strain Sensor Placement Optimization for Wearable Motion Tracking

We present OptiSense, a computational framework that systematically optimizes strain sensor placement using dense motion capture data and genetic algorithm optimization. Our approach discovers counterintuitive sensor configurations that outperform conventional placements and dramatically accelerate model training with 2.5x faster convergence. OptiSense completes optimization in 4.5 hours compared to extensive manual testing and demonstrates consistent ∼33% performance improvements across multiple participants using the same optimized configuration. This work demonstrates that physical sensor configuration is as critical as algorithmic improvements for motion tracking performance.