Open AccessMachine Learning Based Compensation for Inconsistencies in Knitted Force Sensors
Author(s) -
Roland Aigner,
Andreas Stockl
Publication year - 2024
Publication title -
ieee sensors journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.681
H-Index - 121
eISSN - 1558-1748
pISSN - 1530-437X
DOI - 10.1109/jsen.2023.3346686
Subject(s) - signal processing and analysis , communication, networking and broadcast technologies , components, circuits, devices and systems , robotics and control systems
Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.