Please use this identifier to cite or link to this item:
DC FieldValue
dc.titleA biomimetic circuit for electronic skin with application in hand prosthesis
dc.contributor.authorRahiminejad, Ehsan
dc.contributor.authorParvizi-Fard, Adel
dc.contributor.authorIskarous, Mark M.
dc.contributor.authorThakor, Nitish V.
dc.contributor.authorAmiri, Mahmood
dc.identifier.citationRahiminejad, 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.
dc.description.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.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rightsAttribution 4.0 International
dc.sourceScopus OA2021
dc.subjectCMOS implementation
dc.subjectCutaneous afferents
dc.subjectElectronic skin
dc.subjectNeuromorphic circuit
dc.subjectTactile sensing
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.sourcetitleIEEE Transactions on Neural Systems and Rehabilitation Engineering
Appears in Collections:Elements
Staff Publications

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1109_tnsre_2021_3120446.pdf6.05 MBAdobe PDF



Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons