Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger

Sriramana Sankar, Alisa Brown, Darshini Balamurugan, Harrison Nguyen, Mark Iskarous, Talya Simcox, Deepesh Kumar, Andrei Nakagawa, Nitish Thakor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3×3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.

Original languageEnglish (US)
Title of host publication2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116341
DOIs
StatePublished - Oct 2019
Event18th IEEE Sensors, SENSORS 2019 - Montreal, Canada
Duration: Oct 27 2019Oct 30 2019

Publication series

NameProceedings of IEEE Sensors
Volume2019-October
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229

Conference

Conference18th IEEE Sensors, SENSORS 2019
Country/TerritoryCanada
CityMontreal
Period10/27/1910/30/19

Keywords

  • Flexible tactile sensor array
  • Neuromorphic model
  • Soft biomimetic finger
  • Supervised learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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