TY - JOUR
T1 - An extreme learning machine-based neuromorphic tactile sensing system for texture recognition
AU - Rasouli, Mahdi
AU - Chen, Yi
AU - Basu, Arindam
AU - Kukreja, Sunil L.
AU - Thakor, Nitish V.
N1 - Funding Information:
Manuscript received October 22, 2017; revised January 6, 2018; accepted January 10, 2018. Date of publication March 13, 2018; date of current version March 22, 2018. This work was supported by the Office of Naval Research, Arlington, VA, USA, under Grant NICOP N62909-15-1-2024. This paper was recommended by Associate Editor S. Ostadabbas. (Corresponding author: Mahdi Rasouli.) M. Rasouli is with the Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 117456, and also with Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore 117456 (e-mail: rasouli@u.nus.edu).
Publisher Copyright:
© 2007-2012 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - Extreme learning machine
KW - neuromorphic
KW - pattern recognition
KW - tactile perception
KW - texture
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U2 - 10.1109/TBCAS.2018.2805721
DO - 10.1109/TBCAS.2018.2805721
M3 - Article
C2 - 29570059
AN - SCOPUS:85043778555
SN - 1932-4545
VL - 12
SP - 313
EP - 325
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 2
ER -