Reliable Piezoresistive Strain Sensing Through Physical Limits and Uncertainty Monitoring

2026-08-16

AI Summary

This study addresses the sensing unreliability of flexible piezoresistive sensors caused by nonlinearity, hysteresis, and over-range conditions. We propose a reliability framework integrating physical constraints with uncertainty quantification to enable adaptive, trustworthy strain perception without requiring fault samples. By constructing a physics-guided probabilistic inverse model alongside a tri-state risk monitor for strain limits, the system achieves robust performance under complex operating conditions. Experimental results demonstrate a model fitting coefficient of 0.95, an RMSE as low as 0.15%, a 100% anomaly detection rate, and 96% empirical coverage. This work effectively mitigates sensing failures in demanding environments, significantly enhancing the robustness and trustworthiness of flexible sensing systems.

Abstract

Soft piezoresistive strain sensors are one of the most common sensing solutions for wearable and soft robotic applications due to their flexibility and compliance. However, their resistance response is nonlinear and hysteretic, and a sensor can be pushed past its calibrated workspace or misbehave inside it, carrying that error into a decision or control loop. Probabilistic regressors track confidence but ignore those limits. A predictive mean can look unremarkable even when the reading comes from a sensor outside its admissible range or already failing internally, so a confident-looking estimate is not the same as a trustworthy one. This paper proposes a reliability framework pairing a physics-informed probabilistic inverse model, built on physics-guided input features, with a risk factor fusing uncertainty with strain and strain-rate limits into a three-state monitor. Tests on a Nitinol wire and a silver-coated polyamide thread with a Gaussian Process raised fit scores to 0.90-0.95 (RMSE 0.26%-0.15%) and a 96% empirical coverage against the 95% target. The monitor caught 95% of out-of-range and 100% of abnormal conditions while staying reliable under nominal operation. A sensor that reports confidence alongside its estimate lets a system withhold action instead, since it needs no labeled failure examples, which are hard to collect for soft materials.

Problem

• Piezoresistive strain sensor
• Reliability monitoring
• Uncertainty quantification
• Physical limits
• Soft robotics

Innovation

• Physics-informed probabilistic model
• Uncertainty monitoring
• Reliability framework
• Piezoresistive strain sensor
• Three-state monitor