Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

A novel approach to wearable motion tracking that redefines sensor placement strategies is presented. While strain sensors offer compelling advantages over camera‐based systems, most existing methods still rely on intuition‐driven placement and complex machine‐learning models that require extensive data and often generalize poorly. Recent sensor placement optimization studies have attempted to address these limitations using feature selection or search‐based methods, yet they remain constrained to fixed sensor arrays or task‐specific models that do not evaluate placement quality within an end‐to‐end motion tracking framework. This approach overcomes these limitations by leveraging computational strain mapping of joint motion and a genetic algorithm guided directly by model performance to identify optimal sensor configurations that traditional heuristics overlook. The method reveals counterintuitive yet highly effective placements, achieving a 32% reduction in tracking error compared to heuristic layouts. Moreover, this computational framework automatically determines optimal prestrain values, resolving a well‐known limitation in strain sensor deployment. This data‐driven framework not only delivers superior tracking performance but also dramatically accelerates the sensor configuration process, completing in hours what would traditionally require extensive manual testing, thereby enhancing wearable‐sensor design by improving accuracy, efficiency, and practicality for applications in rehabilitation, sports science, and human–computer interaction.