Strain in Sound: Soft Corrugated Tube for Local Strain Sensing with Acoustic Resonance

TL;DR

This paper introduces a soft corrugated tube sensor that uses acoustic resonance and machine learning to accurately estimate local strain in soft structures, demonstrated on a mannequin finger.

Contribution

The novel design combines acoustic resonance with machine learning for precise local strain sensing in soft materials, enabling differentiation of multi-joint deformations.

Findings

Achieved a mean absolute error of 0.8 mm with dual-period tubes.

Single-period tube achieved a 1 mm MAE.

Successfully differentiated multi-joint configurations on a mannequin finger.

Abstract

We present a soft corrugated tube sensor designed to estimate strain in each half segment. When air flows through the tube, the internal corrugated cavities induce pressure oscillations that excite the tube's standing wave resonance mode, generating an acoustic tone. Stretching the tube affects both the resonance mode frequency, due to changes in overall length, and the frequency-flow speed relationship, due to variations in cavity width, which is particularly useful for local strain estimation. By sweeping flow rates in a controlled manner, we collected resonance frequency data across flow speeds under various local stretch conditions, enabling a machine learning algorithm (gradient boosting regressor) to estimate segmental strain with high accuracy. The dual-period tube design (3.1 mm and 4.18 mm corrugation periods) achieved a mean absolute error (MAE) of 0.8 mm, while the single-period tube (3.1 mm) provided a satisfactory MAE of 1 mm. Testing on a mannequin finger demonstrated the sensor's capability to differentiate multi-joint configurations, showing its potential for estimating non-uniform deformations in soft bodies.