Please use this identifier to cite or link to this item: https://doi.org/10.1109/tnsre.2021.3120446
Title: A biomimetic circuit for electronic skin with application in hand prosthesis
Authors: Rahiminejad, Ehsan
Parvizi-Fard, Adel
Iskarous, Mark M.
Thakor, Nitish V. 
Amiri, Mahmood 
Keywords: CMOS implementation
Cutaneous afferents
Electronic skin
Neuromorphic circuit
Tactile sensing
Issue Date: 1-Jan-2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Rahiminejad, Ehsan, Parvizi-Fard, Adel, Iskarous, Mark M., Thakor, Nitish V., Amiri, Mahmood (2021-01-01). A biomimetic circuit for electronic skin with application in hand prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 : 2333-2344. ScholarBank@NUS Repository. https://doi.org/10.1109/tnsre.2021.3120446
Rights: Attribution 4.0 International
Abstract: — One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume 3.5?W of power. The occupied silicon area is 180?m × 220?m for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Source Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/232625
ISSN: 1534-4320
DOI: 10.1109/tnsre.2021.3120446
Rights: Attribution 4.0 International
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