  We propose a sensorization method for soft pneumatic actuators that uses an
embedded microphone and speaker to measure different actuator properties. The
physical state of the actuator determines the specific modulation of sound as
it travels through the structure. Using simple machine learning, we create a
computational sensor that infers the corresponding state from sound recordings.
We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and
use it to measure contact locations, contact forces, object materials, actuator
inflation, and actuator temperature. We show that the sensor is reliable
(average classification rate for six contact locations of 93%), precise (mean
spatial accuracy of 3.7 mm), and robust against common disturbances like
background noise. Finally, we compare different sounds and learning methods and
achieve best results with 20 ms of white noise and a support vector classifier
as the sensor model.
