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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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Texture recognition based on multi-sensory integration of proprioceptive and tactile signals. Sci Rep 2022; 12:21690. [PMID: 36522364 PMCID: PMC9755227 DOI: 10.1038/s41598-022-24640-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022] Open
Abstract
The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors' outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.
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Sagara M, Nobuyama L, Takemura K. Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing. SENSORS (BASEL, SWITZERLAND) 2022; 22:6697. [PMID: 36081155 PMCID: PMC9460129 DOI: 10.3390/s22176697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 06/15/2023]
Abstract
Tactile sensing has attracted significant attention as a tactile quantitative evaluation method because the tactile sensation is an important factor while evaluating consumer products. Although the human tactile perception mechanism has nonlinearity, previous studies have often developed linear regression models. In contrast, this study proposes a nonlinear tactile estimation model that can estimate sensory evaluation scores from physical measurements. We extracted features from the vibration data obtained by a tactile sensor based on the perceptibility of mechanoreceptors. In parallel, a sensory evaluation test was conducted using 10 evaluation words. Then, the relationship between the extracted features and the tactile evaluation results was modeled using linear/nonlinear regressions. The best model was concluded by comparing the mean squared error between the model predictions and the actual values. The results imply that there are multiple evaluation words suitable for adopting nonlinear regression models, and the average error was 43.8% smaller than that of building only linear regression models.
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Affiliation(s)
- Momoko Sagara
- Graduate School of Science for Open and Environmental Systems, Keio University, Yokohama 223-8522, Japan
| | - Lisako Nobuyama
- Graduate School of Science for Open and Environmental Systems, Keio University, Yokohama 223-8522, Japan
| | - Kenjiro Takemura
- Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan
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Yu H, Zhu Y, Zhu L, Lin X, Wan Q. Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2022.954165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.
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Li Y, Yin K, Diao Y, Fang M, Yang J, Zhang J, Cao H, Liu X, Jiang J. A biopolymer-gated ionotronic junctionless oxide transistor array for spatiotemporal pain-perception emulation in nociceptor network. NANOSCALE 2022; 14:2316-2326. [PMID: 35084010 DOI: 10.1039/d1nr07896h] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Capable of reflecting the location and intensity of external harmful stimuli, a nociceptor network is of great importance for receiving pain-perception information. However, the hardware-based implementation of a nociceptor network through the use of a transistor array remains a great challenge in the area of brain-inspired neuromorphic applications. Herein, a simple ionotronic junctionless oxide transistor array with pain-perception abilities is successfully realized due to a coplanar-gate proton-coupling effect in sodium alginate biopolymer electrolyte. Several important pain-perception characteristics of nociceptors are emulated, such as a pain threshold, the memory of prior injury, and sensitization behavior due to pathway alterations. In particular, a good graded pain-perception network system has been successfully established through coplanar capacitance and resistance. More importantly, clear polarity reversal of Lorentz-type spatiotemporal pain-perception emulation can be finally realized in our projection-dependent nociceptor network. This work may provide new avenues for bionic medical machines and humanoid robots based on these intriguing pain-perception abilities.
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Affiliation(s)
- Yanran Li
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Kai Yin
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Yu Diao
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Mei Fang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Junliang Yang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Jian Zhang
- School of Material Science and Engineering, Guilin University of Electronic Technology, Guilin, 541004, P. R. China
| | - Hongtao Cao
- Laboratory of Advanced Nano Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P. R. China
| | - Xiaoliang Liu
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
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Xia Q, Qin Y, Qiu P, Zheng A, Zhang X. Bio‑inspired Tactile Nociceptor Constructed by Integrating Wearable Sensing Paper and VO2 Threshold Switching Memristor. J Mater Chem B 2022; 10:1991-2000. [DOI: 10.1039/d1tb02578c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The sensations of touch and pain are fundamental components of our daily life, which can transport vital information about the surroundings and provide protection to our bodies. In this study,...
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Rahiminejad E, Parvizi-Fard A, Iskarous MM, Thakor NV, Amiri M. A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2333-2344. [PMID: 34673491 DOI: 10.1109/tnsre.2021.3120446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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 [Formula: see text] of power. The occupied silicon area is [Formula: see text] 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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