In this paper, a data-driven approach is presented to calibrate and compensate for degradation effects in conductive textile sensors on microcontrollers. An experimental setup was constructed to control the elongation of textile strain sensors during prolonged use. The recorded course of resistance in combination with elongation and applied force was used to generate an initial model.

During the use of the textile sensor, degradation effects occurred, which altered the sensor characteristics. A machine learning approach on a embedded system was used to retrain the internal model without knowledge about the true or measured deformation. The performance of the initial model is compared to an online retraining algorithm for recognition and compensation without a connection to the sensors from the test rig or database. All adaptions to the internal model are calculated on a resource-limited embedded microcontroller. Furthermore, this paper highlights the current requirements and possibilities for such mobile calibration or re-training methods.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 9 May 2023
EventInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023) - Tenerife , Santa Cruz de Tenerife, Spain
Duration: 19 Jul 202321 Jul 2023
Conference number: 57830


ConferenceInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023)
Abbreviated titleICECCME 2023
CitySanta Cruz de Tenerife
Internet address


  • Machine Learning
  • Conductive Textiles
  • Edge Computing
  • Smart Systems
  • Retraining


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