Strain in Sound: Soft Corrugated Tube for Local Strain Sensing with Acoustic Resonance
This work addresses strain sensing for soft robotics or wearable devices, offering a novel sensor design but is incremental in combining acoustic resonance with machine learning.
The researchers tackled the problem of local strain sensing in soft bodies by developing a soft corrugated tube sensor that uses acoustic resonance and machine learning, achieving a mean absolute error of 0.8 mm for dual-period and 1 mm for single-period designs.
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.