Force Localization Estimation Using a Designed Soft Tactile Sensor

  • Merve AcerEmail author
  • Adnan Furkan Yıldız
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 22)


Wearable tactile sensors are significant in biomedical robotic applications where force feedback is important. In this work, a soft tactile sensor is proposed for force localization. The tactile sensor was manufactured by using layer-by-layer technique that enables flexibility. The sensor has 9 lead zirconate titanate (PZT) elements placed in 3 × 3 matrix form which are 4 × 4 mm2 and the spatial resolution is 3 mm. The voltage values gathered from the sensor were conditioned by a charge amplifier circuit. A human inspired machine learning procedure called Neural Networks was used for force localization. The success rates with respect to different network structures were presented and the maximum success was realized as 80.71%.


  1. 1.
    Tiwana, M.I., Redmond, S.J., Lovell, N.H.: A review of tactile sensing technologies with applications in biomedical engineering. Sens. Actuators A Phys. 179, 17–31 (2012)CrossRefGoogle Scholar
  2. 2.
    Kenry, J.C.Y., Lim, C.T.: Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications. Microsyst. Nanoeng. 2, 16043 (2016)CrossRefGoogle Scholar
  3. 3.
    Acer, M., Yıldız, A.F., Bazzaz, F.H.: Development of a soft PZT based tactile sensor array for force localization. In: 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, pp. 1–6 (2017)Google Scholar
  4. 4.
    Karki, J.: Signal conditioning piezoelectric sensors. Application Report on Mixed Signal Products, Texas Instruments IncorporatedGoogle Scholar
  5. 5.
    Werbos, P.J.: Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, Cambridge, MA (1974)Google Scholar
  6. 6.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  7. 7.
    Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing, Boston (1996)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mechanical Engineering Departmentİstanbul Technical UniversityİstanbulTurkey

Personalised recommendations