A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis

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 propo...

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Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering 2021, Vol.29, p.2333-2344
Hauptverfasser: Rahiminejad, Ehsan, Parvizi-Fard, Adel, Iskarous, Mark M., Thakor, Nitish V., Amiri, Mahmood
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container_title IEEE transactions on neural systems and rehabilitation engineering
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creator Rahiminejad, Ehsan
Parvizi-Fard, Adel
Iskarous, Mark M.
Thakor, Nitish V.
Amiri, Mahmood
description 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\mu \text{W} of power. The occupied silicon area is 180\mu \text{m}\,\,\times 220\mu \text{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.
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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 <inline-formula> <tex-math notation="LaTeX">3.5\mu \text{W} </tex-math></inline-formula> of power. The occupied silicon area is <inline-formula> <tex-math notation="LaTeX">180\mu \text{m}\,\,\times 220\mu \text{m} </tex-math></inline-formula> 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. 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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 <inline-formula> <tex-math notation="LaTeX">3.5\mu \text{W} </tex-math></inline-formula> of power. The occupied silicon area is <inline-formula> <tex-math notation="LaTeX">180\mu \text{m}\,\,\times 220\mu \text{m} </tex-math></inline-formula> 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. 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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 <inline-formula> <tex-math notation="LaTeX">3.5\mu \text{W} </tex-math></inline-formula> of power. The occupied silicon area is <inline-formula> <tex-math notation="LaTeX">180\mu \text{m}\,\,\times 220\mu \text{m} </tex-math></inline-formula> 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.]]></abstract><cop>New York</cop><pub>IEEE</pub><pmid>34673491</pmid><doi>10.1109/TNSRE.2021.3120446</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-1720-0060</orcidid><orcidid>https://orcid.org/0000-0003-2081-0134</orcidid><orcidid>https://orcid.org/0000-0002-9981-9395</orcidid><orcidid>https://orcid.org/0000-0003-4208-3943</orcidid><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Biomimetics
Circuit design
CMOS
CMOS implementation
cutaneous afferents
electronic skin
Firing
Firing pattern
Indentation
Innervation
Learning algorithms
Machine learning
Mechanoreceptors
neuromorphic circuit
Neuromorphics
Power consumption
Prostheses
Prosthetics
Radio frequency
Robot arms
Robots
Sensors
Sensory feedback
Sensory neurons
Skin
Spiking
Tactile sensing
Tactile sensors
Tactile sensors (robotics)
Three dimensional printing
title A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis
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