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This paper introduces a soft corrugated tube sensor that uses acoustic resonance to estimate local strain by analyzing changes in resonance frequency and frequency-flow speed relationship under varying stretch conditions. A gradient boosting regressor is trained on resonance frequency data collected across different flow speeds and local stretch conditions to estimate segmental strain. Results show the dual-period tube design achieved a mean absolute error (MAE) of 0.8 mm, demonstrating the sensor's potential for estimating non-uniform deformations in soft bodies.
Acoustic resonance in a corrugated tube reveals local strain with sub-millimeter accuracy, offering a simple, soft alternative to traditional strain gauges.
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.