Machine Learning Based Compensation for Inconsistencies in Knitted Force Sensors
This addresses reliability issues for knitted sensors in applications like wearables or robotics, but it is incremental as it builds on existing methods.
The paper tackled inconsistencies in knitted force sensors, such as offset and drift, by using a minimal neural network with exponential smoothing filters, achieving significant improvement in mapping sensor readings to actuation force.
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.