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

TL;DR

This paper compares IR rangefinding and Hall Effect magnetic sensing methods, combined with neural networks, to accurately estimate compression in soft robotic bladders for smart helmets, enhancing impact control.

Contribution

It introduces a novel approach combining IR and Hall Effect sensors with neural networks for precise bladder compression estimation in smart helmets.

Findings

IR rangefinder with neural networks can estimate compression effectively.

Hall Effect sensors combined with neural networks provide accurate predictions.

Different training datasets and NN configurations impact estimation accuracy.

Abstract

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