Spike-based tactile pattern recognition using an extreme learning machine

Mahdi Rasouli, Chen Yi, Arindam Basu, Nitish V Thakor, Sunil Kukreja

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


We present a biologically-inspired approach for tactile pattern recognition. Our aim is to develop a low-cost tactile module that can be applied to large areas by integrating sensors with processing circuits. To accomplish this goal a flexible tactile sensor array was developed using piezoresistive fabric material. The output of the tactile array was represented as a spatiotemporal spike pattern to emulate neural signals from mechanoreceptors in the skin. A hardware implemented Extreme Learning Machine (ELM) was used to process the tactile information. The ELM chip is an event-driven system that is massively parallel and energy-efficient. For these reasons, our proposed architecture offers a fast and energy-efficient alternative for processing spatiotemporal tactile patterns. The performance of the system was evaluated during a real-Time object classification task, where it achieved 90% accuracy for binary classification.

Original languageEnglish (US)
Title of host publicationIEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479972333
StatePublished - Dec 4 2015
Event11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States
Duration: Oct 22 2015Oct 24 2015


Other11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
CountryUnited States

ASJC Scopus subject areas

  • Biotechnology
  • Instrumentation
  • Biomedical Engineering
  • Electrical and Electronic Engineering

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