Knit-Structure Effects on Electromechanical Metrics and Their Correlation with Joint-Angle Estimation Error in Knitted Strain Sensors

2026-10-05

AI Summary

This study addresses the unclear relationship between the structural design of knitted strain sensors and their actual joint motion sensing performance. By integrating uniaxial cyclic tensile testing, simulated joint flexion, and machine learning models, it systematically investigates how knitting structural parameters influence electromechanical metrics and correlate with angle estimation errors. The findings reveal that the gauge factor and baseline resistance are critical predictors of estimation error, whereas other metrics exhibit weak correlations. Furthermore, this work demonstrates that simple tensile tests can effectively screen out suboptimal sensor designs. Ultimately, this research provides a theoretical foundation and an efficient evaluation paradigm for optimizing sensor designs tailored to specific applications.

Abstract

Knitted resistive strain sensors show strong promise for joint motion sensing in sports and rehabilitation, but the linkage between sensor design and in situ performance remains unclear. We investigate how knit structure and machine settings (e.g., stitch size) shape electromechanical properties and which metrics predict sensing performance during bending. Sensors spanning seven common knit structures at two stitch sizes were fabricated, characterized under uniaxial cyclic tension, and evaluated on a joint emulating bending rig. Joint-angle estimation was assessed with machine learning models, and correlations with electromechanical metrics were analyzed. Experimental results show that, among six common metrics, gauge factor and baseline resistance are largely set by knit structure, while working range, linear range, hysteresis, and cyclic stability vary only modestly across designs. Gauge factor correlates negatively and baseline resistance positively with joint-angle estimation error, mainly in lower-sensitivity designs, whereas the other metrics have weak or no predictive value. These results support using uniaxial tensile tests to screen out weak designs, while underscoring the need for joint-relevant evaluation and application-specific metrics to identify top performers.

Problem

• knitted strain sensors
• electromechanical metrics
• joint-angle estimation
• knit structure
• bending performance

Innovation

• Knitted Strain Sensors
• Electromechanical Metrics
• Joint-Angle Estimation
• Machine Learning
• Knit Structure