Machine Learning Based Compensation for Inconsistencies in Knitted Force Sensors

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1 Citation (Scopus)


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 article, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward preprocessing. We apply a number of exponential smoothing filters on a resampled 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 eight 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.

Original languageEnglish (American)
Pages (from-to)4899-4906
Number of pages8
JournalIEEE Sensors Journal
Issue number4
Publication statusPublished - 15 Feb 2024


  • Artificial neural networks
  • Force
  • Hysteresis
  • Sensor phenomena and characterization
  • Sensors
  • Substrates
  • Yarn
  • filtering
  • force sensor
  • knitting
  • machine learning
  • neuronal networks
  • resistive sensing
  • textile sensor
  • Filtering


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