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This research focuses on soft robotic bladders that are used to monitor andcontrol the interaction between a user's head and the shell of a Smart Helmet.Compression of these bladders determines impact dissipation; hence the focus ofthis paper is sensing and estimation of bladder compression. An IRrangefinder-based solution is evaluated using regression techniques as well asa Neural Network to estimate bladder compression. A Hall-Effect (HE) magneticsensing system is also examined where HE sensors embedded in the base of thebladder sense the position of a magnet in the top of the bladder. The paperpresents the HE sensor array, signal processing of HE voltage data, and then aNeural Network (NN) for predicting bladder compression. Efficacy of differenttraining data sets on NN performance is studied. Different NN configurationsare examined to determine a configuration that provides accurate estimates withas few nodes as possible. Different bladder compression profiles are evaluatedto characterize IR range finding and HE based techniques in applicationscenarios.

Estimation of Soft Robotic Bladder Compression for Smart Helmets using IR Range Finding and Hall Effect Magnetic Sensing