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|Title:||An extreme learning machine-based neuromorphic tactile sensing system for texture recognition||Authors:||Rasouli, Mahdi
Kukreja, Sunil L.
Thakor, Nitish V.
Extreme Learning Machine
Engineering::Electrical and electronic engineering
|Issue Date:||2018||Source:||Rasouli, M., Chen, Y., Basu, A., Kukreja, S. L., & Thakor, N. V. (2018). An extreme learning machine-based neuromorphic tactile sensing system for texture recognition. IEEE Transactions on Biomedical Circuits and Systems, 12(2), 313-325. doi:10.1109/TBCAS.2018.2805721||Series/Report no.:||IEEE Transactions on Biomedical Circuits and Systems||Abstract:||Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.||URI:||https://hdl.handle.net/10356/107523
|ISSN:||1932-4545||DOI:||10.1109/TBCAS.2018.2805721||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: http://dx.doi.org/10.1109/TBCAS.2018.2805721.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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