International Journal of Extreme Manufacturing, Volume. 7, Issue 4, 42002(2025)

Integration of AI with artificial sensory systems for multidimensional intelligent augmentation

Tian Changyu, Cho Youngwook, Song Youngho, Park Seongcheol, Kim Inho, and Cho Soo-Yeon
References(159)

[1] [1] Jung Y H, Park B, Kim J U and Kim T I. 2019. Bioinspired electronics for artificial sensory systems.Adv. Mater.31, 1803637.

[2] [2] Cao Z C et al. 2024. A programmable electronic skin with event-driven in-sensor touch differential and decision-making.Adv. Funct. Mater.35, 2412649.

[3] [3] Wang L L, Xie J L, Wang Q W, Hu J J, Jiang Y W, Wang J J, Tong H R, Yuan H B and Yang Y Q. 2024. Evaluation of the quality grade of congou black tea by the fusion of GC-E-nose,E-tongue, andE-eye.Food Chem.X23, 101519.

[4] [4] Abbasi J. 2019. “Electronic nose” predicts immunotherapy response.JAMA322, 1756.

[5] [5] Kerdvibulvech C. 2016. A novel integrated system of visual communication and touch technology for people with disabilities.In Proceedings of the 16th International Conference on Computational Science and Its Applications(Springer, Beijing, China) pp 509–518.

[6] [6] Guerrini L, Garcia-Rico E, Pazos-Perez N and Alvarez-Puebla R A. 2017. Smelling, seeing, tasting- old senses for new sensing.ACS Nano11, 5217–5222.

[7] [7] Zhou A R, Jie X L, Yang Y, Hu J M and Lin S L. 2024. Flavor analysis of three kinds of edible fungus plant steaks by electronic sensory technology combined with artificial sensory evaluation.Fujian Agric. Sci. Technol.55, 1–7.

[8] [8] Wang B J et al. 2024. Body-integrated ultrasensitive all-textile pressure sensors for skin-inspired artificial sensory systems.Small Sci.4, 2400026.

[9] [9] Zhao S and Zhu R. 2018. A smart artificial finger with multisensations of matter, temperature, and proximity.Adv. Mater. Technol.3, 1800056.

[10] [10] Wan C J, Cai P Q, Guo X T, Wang M, Matsuhisa N, Yang L, Lv Z S, Luo Y F, Loh X J and Chen X D. 2020. An artificial sensory neuron with visual-haptic fusion.Nat. Commun.11, 4602.

[11] [11] Veltink P H. 1999. Sensory feedback in artificial control of human mobility.Technol. Health Care7, 383–391.

[12] [12] Wang M, Luo Y F, Wang T, Wan C J, Pan L, Pan S W, He K, Neo A and Chen X D. 2021. Artificial skin perception.Adv. Mater.33, 2003014.

[13] [13] Wang C F, Dong L, Peng D F and Pan C F. 2019. Tactile sensors for advanced intelligent systems.Adv. Intell. Syst.1, 1900090.

[14] [14] Xie D S, Peng W, Chen J C, Li L, Zhao C B, Yang S L, Xu M, Wu C J and Ai L. 2016. A novel method for the discrimination of Hawthorn and its processed products using an intelligent sensory system and artificial neural networks.Food Sci. Biotechnol.25, 1545–1550.

[15] [15] Yu H L, Zhu Y X, Zhu L, Lin X H and Wan Q. 2022. Recent advances in transistor-based bionic perceptual devices for artificial sensory systems.Front. Nanotechnol.4, 954165.

[16] [16] Pantke F, Bosse S, Lawo M, Lehmhus D and Busse M 2011. An artificial intelligence approach towards sensorial materials.In Proceedings of the Third International Conference on Future Computational Technologies and Applications. (IARIA, Rome, Italy).

[17] [17] Chen H, Cai Y H, Han Y H and Huang H. 2024. Towards artificial visual sensory system: organic optoelectronic synaptic materials and devices.Angew. Chem., Int. Ed.63, e202313634.

[18] [18] Kwon J Y, Kim J E, Kim J S, Chun S Y, Soh K and Yoon J H. 2024. Artificial sensory system based on memristive devices.Exploration4, 20220162.

[19] [19] Song O, Cho Y, Cho S Y and Kang J. 2024. Solution-processing approach of nanomaterials toward an artificial sensory system.Int. J. Extrem. Manuf.6, 052001.

[20] [20] Kang H, Cho S Y, Ryu J, Choi J, Ahn H, Joo H and Jung H T. 2020. Multiarray nanopattern electronic nose (E-Nose) by high-resolution top-down nanolithography.Adv. Funct. Mater.30, 2002486.

[21] [21] Bruno C, Licciardello A, Nastasi G A M, Passaniti F, Brigante C, Sudano F, Faulisi A and Alessi E. 2021. Embedded artificial intelligence approach for gas recognition in smart agriculture applications using low cost MOX gas sensors.In Proceedings of 2021 Smart Systems Integration. (IEEE, Grenoble, France). pp 1–5.

[22] [22] Wang J W, Wang C, Cai P Q, Luo Y F, Cui Z Q, Loh X J and Chen X D. 2021. Artificial sense technology: emulating and extending biological senses.ACS Nano15, 18671–18678.

[23] [23] Ma Y J et al. 2020. Flexible hybrid electronics for digital healthcare.Adv. Mater.32, 1902062.

[24] [24] Cho S Y and Jung H T. 2023. Artificial intelligence: a game changer in sensor research.ACS Sens.8, 1371–1372.

[25] [25] Yao J Y, Zhao W J, Bai X Y, Wan P, Liu J and Chen D W. 2023. Non-volatile taste active compounds in the meat of river snail (Sinotaia quadrata) determined by 1H NMR, etongue and sensory analysis.Int. J. Gastron. Food. Sci.34, 100803.

[26] [26] Long Z H et al. 2023. A neuromorphic bionic eye with filterfree color vision using hemispherical perovskite nanowire array retina.Nat. Commun.14, 1972.

[27] [27] Li S Y et al. 2024. An all-protein multisensory highly bionic skin.ACS Nano18, 4579–4589.

[28] [28] Yang C K, Xiang Y, Liao B and Hu X R. 2023. 3D-printed bionic ear for sound identification and localization based onin situpolling of PVDF-TrFE film.Macromol. Biosci.23, 2200374.

[29] [29] Manjula R, Narasamma B, Shruthi G, Nagarathna K and Kumar G. 2021. Artificial olfaction for detection and classification of gases using E-Nose and machine learning for industrial application.In Machine Intelligence and Data Analytics for Sustainable Future Smart Cities(eds Ghosh U, Maleh Y, Alazab M and Pathan A S K). (Springer, Cham). pp 35–48.

[30] [30] Kozawa D et al. 2020. A fiber optic interface coupled to nano-sensors: applications to protein aggregation and organic molecule quantification.ACS Nano14, 10141–10152.

[31] [31] Nie B Q, Liu S D, Qu Q, Zhang Y Q, Zhao M Y and Liu J. 2022. Bio-inspired flexible electronics for smart E-skin.Acta Biomater.139, 280–295.

[32] [32] Yang Z Y, Liu Y M, Chen D, Miao J M, Chen M R, Liu G, Gao G, Guo Y P, Cui D X and Li Q C. 2024. A batteryfree, wireless, flexible bandlike e-nose based on MEMS gas sensors for precisely volatile organic compounds detection.Nano Energy127, 109711.

[33] [33] Gong Y, Xing X C, Wang X L, Duan R H, Han S T and Tay B K. 2024. Integrated bionic human retina process and in-sensor RC system based on 2D retinomorphic memristor array.Adv. Funct. Mater.34, 2406547.

[34] [34] Zhang H Z et al. 2023. A neuromorphic bionic eye with broadband vision and biocompatibility using TIPS-pentacene-based phototransistor array retina.Appl. Mater. Today33, 101885.

[35] [35] Gou G-Y et al. 2022. Two-stage amplification of an ultrasensitive MXene-based intelligent artificial eardrum.Sci. Adv.8, eabn2156.

[36] [36] Zang J B, Zhou C Z, Xiang M H, Wang J L, Wang H X, Zhang Z D and Xue C Y. 2022. Optimum design and test of a novel bionic electronic stethoscope based on the cruciform microcantilever with leaf microelectromechanical systems structure.Adv. Mater. Technol.7, 2101501.

[37] [37] Serrano-Gotarredona T and Linares-Barranco B. 2013. A 128×128 1.5% contrast sensitivity 0.9% FPN 3 μs latency 4 mW asynchronous frame-free dynamic vision sensor using transimpedance preamplifiers.IEEE J. Solid-State Circuits48, 827–838.

[38] [38] Ahmadi H, Moradi H, Pastras C J, Abolpour Moshizi S, Wu S Y and Asadnia M. 2021. Development of ultrasensitive biomimetic auditory hair cells based on piezoresistive hydrogel nanocomposites.ACS Appl. Mater. Interfaces13, 44904–44915.

[39] [39] Jang J, Oh B, Jo S, Park S, An H S, Lee S, Cheong W H, Yoo S and Park J U. 2019. Human-interactive, active-matrix displays for visualization of tactile pressures.Adv. Mater. Technol.4, 1900082.

[40] [40] Yang K, Yin F X, Xia D, Peng H F, Yang J Z and Yuan W J. 2019. A highly flexible and multifunctional strain sensor based on a network-structured MXene/polyurethane mat with ultra-high sensitivity and a broad sensing range.Nanoscale11, 9949–9957.

[41] [41] Kim S J et al. 2018. Metallic Ti3C2Tx MXene gas sensors with ultrahigh signal-to-noise ratio.ACS Nano12, 986–993.

[42] [42] Tian C Y, Lee Y, Song Y, Elmasry M R, Yoon M, Kim D H and Cho S Y. 2024. Machine-learning-enhanced fluorescent nano-sensor based on carbon quantum dots for heavy metal detection.ACS Appl. Nano Mater.7, 5576–5586.

[43] [43] Kang H, Joo H, Choi J, Kim Y J, Lee Y, Cho S Y and Jung H T. 2022. Top-down approaches for 10 nm-scale nanochannel: toward exceptional H2S detection.ACS Nano16, 17210–17219.

[44] [44] Guo X G, Sun Z D, Zhu Y and Lee C. 2024. Zero-biased bionic fingertip E-Skin with multimodal tactile perception and artificial intelligence for augmented touch awareness.Adv. Mater.36, 2406778.

[45] [45] Lee K, Jang S, Kim K L, Koo M, Park C, Lee S, Lee J, Wang G and Park C. 2020. Artificially intelligent tactile ferroelectric skin.Adv. Sci.7, 2001662.

[46] [46] Wan T Q, Shao B J, Ma S J, Zhou Y, Li Q and Chai Y. 2023. Insensor computing: materials, devices, and integration technologies.Adv. Mater.35, 2203830.

[47] [47] Zhou F C and Chai Y. 2020. Near-sensor and in-sensor computing.Nat. Electron.3, 664–671.

[48] [48] Li D L, Wang Y, Wang J X, Wang C and Duan Y Q. 2020. Recent advances in sensor fault diagnosis: a review.Sens. ActuatorsA309, 111990.

[49] [49] Rota-Rodrigo S, Lpez-Aldaba A, Prez-Herrera R A, Del Carmen Lpez Bautista M, Esteban and Lpez-Amo M. 2016. Simultaneous measurement of humidity and vibration based on a microwire sensor system using fast Fourier transform technique.J. Lightwave Technol.34, 4525–4530.

[50] [50] Sasiadek J Z and Hartana P 2000. Sensor data fusion using Kalman filter.In Proceedings of the Third International Conference on Information Fusion. (IEEE, Paris, France).

[51] [51] Qian Y, Cai Q, Pan Y W, Li Y H, Yao T, Sun Q B and Mei T 2024. Boosting diffusion models with moving average sampling in frequency domain.In Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. (IEEE, Seattle, WA, USA). pp 8911–8920.

[52] [52] Ballard Z, Brown C, Madni A M and Ozcan A. 2021. Machine learning and computation-enabled intelligent sensor design.Nat. Mach. Intell.3, 556–565.

[53] [53] Brandt A. 2023.Noise and Vibration Analysis: Signal Analysis and Experimental Procedures. 2nd edn. (John Wiley & Sons, Hoboken).

[54] [54] Kumar A, Tomar H, Mehla V K, Komaragiri R and Kumar M. 2021. Stationary wavelet transform based ECG signal denoising method.ISA Trans.114, 251–262.

[55] [55] Azpeitia E, Balanzario E P and Wagner A. 2020. Signaling pathways have an inherent need for noise to acquire information.BMC Bioinf.21, 462.

[56] [56] Niu J, Xu X P, Pan Y and Duan Z H. 2024. An investigation of a biomimetic optical system and an evaluation model for the qualitative analysis of laser interference visual levels.Biomimetics9, 220.

[57] [57] Cho S Y, Lee Y, Lee S, Kang H, Kim J, Choi J, Ryu J, Joo H, Jung H T and Kim J. 2020. Finding hidden signals in chemical sensors using deep learning.Anal. Chem.92, 6529–6537.

[58] [58] Yoon M, Shin S, Lee S, Kang J, Gong X and Cho S Y. 2024. Scalable photonic nose development through corona phase molecular recognition.ACS Sens.12, 6311–6319.

[59] [59] Salisbury K, Brock D, Massie T, Swarup N and Zilles C 1995. Haptic rendering: programming touch interaction with virtual objects.In Proceedings of the 1995 Symposium on Interactive 3D Graphics. (ACM, Monterey, California, USA). pp 123–130.

[60] [60] Gardner J W. 1991. Detection of vapours and odours from a multisensor array using pattern recognition Part 1. Principal component and cluster analysis.Sens. ActuatorsB4, 109–115.

[61] [61] Gardner J W, Hines E L and Tang H C. 1992. Detection of vapours and odours from a multisensor array using pattern-recognition techniques Part 2. Artificial neural networks.Sens. ActuatorsB9, 9–15.

[62] [62] Sun F Q, Lu Q F, Feng S M and Zhang T. 2021. Flexible artificial sensory systems based on neuromorphic devices.ACS Nano15, 3875–3899.

[63] [63] Hua Q L, Cui X, Liu H T, Pan C F, Hu W G and Wang Z L. 2020. Piezotronic synapse based on a single GaN microwire for artificial sensory systems.Nano Lett.20, 3761–3768.

[64] [64] Wan H C, Zhao J Y, Lo L W, Cao Y Q, Sepu′lveda N and Wang C. 2021. Multimodal artificial neurological sensory–memory system based on flexible carbon nanotube synaptic transistor.ACS Nano15, 14587–14597.

[65] [65] Berco D, Ang D S and Zhang H Z. 2020. An optoneuronic device with realistic retinal expressions for bioinspired machine vision.Adv. Intell. Syst.2, 1900115.

[66] [66] Park S, Yoon S E, Song Y, Tian C Y, Baek C, Cho H, Kim W S, Kim S J and Cho S Y. 2024. A simple approach to biophysical profiling of blood cells in extranodal NK/T cell lymphoma patients using deep learning-integrated image cytometry.BMEMat2, e12128.

[67] [67] Lee C H and Rianto B. 2024. An AI-powered e-nose system using a density-based clustering method for identifying adulteration in specialty coffees.Microchem. J.197, 109844.

[68] [68] Talens J B, Pelegr-Sebasti J, Sogorb T and Ruiz J L. 2023. Prostate cancer detection using e-nose and AI for high probability assessment.BMC Med. Inform. Decis. Mak.23, 205.

[69] [69] Liu J M, Qian J G, Adil M, Bi Y L, Wu H Y, Hu X F, Wang Z K and Zhang W. 2024. Bioinspired integrated triboelectric electronic tongue.Microsyst. Nanoeng.10, 57.

[70] [70] Chen Y F, Lei H, Gao Z Q, Liu J Y, Zhang F J, Wen Z and Sun X H. 2022. Energy autonomous electronic skin with direct temperature-pressure perception.Nano Energy98, 107273.

[71] [71] Attallah O and Morsi I. 2022. An electronic nose for identifying multiple combustible/harmful gases and their concentration levels via artificial intelligence.Measurement199, 111458.

[72] [72] Li W, Xu J J, Yang W R, Liu F L, Zhou H Y and Yan Z H. 2024. Approach and application of extracting matching features from E-nose signals for AI tasks.Biomed. Signal Process. Control90, 105869.

[73] [73] Rong Y and Gu G Y. 2023. Deep transfer learning-based adaptive gesture recognition of a soft e-skin patch with reduced training data and time.Sens. ActuatorsA363, 114693.

[74] [74] Yoon M, Lee Y, Lee S, Cho Y, Koh D, Shin S, Tian C Y, Song Y, Kang J and Cho S Y. 2024. A nIR fluorescent single walled carbon nanotube sensor for broad-spectrum diagnostics.Sens. Diagn.3, 203–217.

[75] [75] Teyssier M, Parilusyan B, Roudaut A and Steimle J 2021. Human-like artificial skin sensor for physical human-robot interaction.In Proceedings of 2021 IEEE International Conference on Robotics and Automation. (IEEE, Xi’an, China). pp 3626–3633.

[76] [76] Heo J H et al. 2023. Sensor design strategy for environmental and biological monitoring.EcoMat5, e12332.

[77] [77] Han X, Huang D, Eun-Lee S and Hoon-Yang J. 2023. Artificial intelligence-oriented user interface design and human behavior recognition based on human–computer nature interaction.Int. J. Human. Robot.20, 2250020.

[78] [78] Yang B, Wei L and Pu Z H. 2020. Measuring and improving user experience through artificial intelligence-aided design.Front. Psychol.11, 595374.

[79] [79] Peebles D, Lu H, Lane N, Choudhury T and Campbell A 2010. Community-guided learning: exploiting mobile sensor users to model human behavior.In Proceedings of the 24th AAAI Conference on Artificial Intelligence. (AAAI Press, Atlanta, Georgia, USA). pp 1600–1606.

[80] [80] Virvou M. 2023. Artificial Intelligence and User Experience in reciprocity: contributions and state of the art.Intell. Decis. Technol.17, 73–125.

[81] [81] Myers K, Berry P, Blythe J, Conley K, Gervasio M, McGuinness D, Morley D, Pfeffer A, Pollack M and Tambe M. 2007.AI Mag.28, 47–61.

[82] [82] Murali P K, Kaboli M and Dahiya R. 2022. Intelligent in-vehicle interaction technologies.Adv. Intell. Syst.4, 2100122.

[83] [83] Tian C Y, Shin S, Cho Y, Song Y and Cho S Y. 2024. High spatiotemporal precision mapping of optical nano-sensor array using machine learning.ACS Sens.9, 5489–5499.

[84] [84] Wen D Z, Li X Y, Zhou Y, Shi Y M, Wu S and Jiang C X. 2024. Integrated sensing-communication-computation for edge artificial intelligence.IEEE Internet Things Mag.7, 14–20.

[85] [85] Chen H, Huo D X and Zhang J L. 2022. Gas recognition in E-nose system: a review.IEEE Trans. Biomed. Circuits Syst.16, 169–184.

[86] [86] Robertsson L, Iliev B, Palm R and Wide P. 2007. Perception modeling for human-like artificial sensor systems.Int. J. Hum.-Comput. Stud.65, 446–459.

[87] [87] Bryndin E. 2020. Development of sensitivity and active behavior of cognitive robot by means artificial intelligence.Int. J. Robot. Res. Dev.10, 1–11.

[88] [88] Wu X C, Jiang L L, Xu H H, Wang B H, Yang L, Wang X H, Zheng L, Xu W T and Qiu L Z. 2024. Bionic olfactory synaptic transistors for artificial neuromotor pathway construction and gas recognition.Adv. Funct. Mater.34, 2401965.

[89] [89] Zheng W D, Liu H P, Guo D and Sun F C. 2022. Robust tactile object recognition in open-set scenarios using Gaussian prototype learning.Front. Neurosci.16, 1070645.

[90] [90] Sun L F, Zhang Y S, Hwang G, Jiang J B, Kim D, Eshete Y A, Zhao R and Yang H. 2018. Synaptic computation enabled by joule heating of single-layered semiconductors for sound localization.Nano Lett.18, 3229–3234.

[91] [91] Calvini R and Pigani L. 2022. Toward the development of combined artificial sensing systems for food quality evaluation: a review on the application of data fusion of electronic noses, electronic tongues and electronic eyes.Sensors22, 577.

[92] [92] liwiska M, Winiewska P, Dymerski T, Namienik J and Wardencki W. 2014. Food analysis using artificial senses.J. Agric. Food Chem.62, 1423–1448.

[93] [93] Jung H H et al. 2023. Taste bud-inspired single-drop multitaste sensing for comprehensive flavor analysis with deep learning algorithms.ACS Appl. Mater. Interfaces15, 46041–46053.

[94] [94] Niu H S, Li H, Gao S, Li Y, Wei X, Chen Y K, Yue W J, Zhou W J and Shen G Z. 2022. Perception-to-cognition tactile sensing based on artificial-intelligence-motivated human full-skin bionic electronic skin.Adv. Mater.34, 2202622.

[95] [95] Jiang Y, Zhang Y F, Ning C, Ji Q Q, Peng X, Dong K and Wang Z L. 2022. Ultrathin eardrum-inspired self-powered acoustic sensor for vocal synchronization recognition with the assistance of machine learning.Small18, 2106960.

[96] [96] Bai N N et al. 2023. A robotic sensory system with high spatiotemporal resolution for texture recognition.Nat. Commun.14, 7121.

[97] [97] Niu H S, Yin F F, Kim E S, Wang W X, Yoon D Y, Wang C, Liang J G, Li Y and Kim N Y. 2023. Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence.InfoMat5, e12412.

[98] [98] Bag A, Ghosh G, Sultan M J, Chouhdry H H, Hong S J, Trung T Q, Kang G Y and Lee N E. 2024. Bio-inspired sensory receptors for artificial-intelligence perception.Adv. Mater.36, 2403150.

[99] [99] Li Y F, Wu F X and Ngom A. 2018. A review on machine learning principles for multi-view biological data integration.Brief Bioinform.19, 325–340.

[100] [100] Zhu Y M, Wang M, Yin X F, Zhang J, Meijering E and Hu J K. 2022. Deep learning in diverse intelligent sensor based systems.Sensors23, 62.

[101] [101] Leong Y X, Lee Y H, Koh C S L, Phan-Quang G C, Han X M, Phang I Y and Ling X Y. 2021. Surface-enhanced Raman scattering (SERS) taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors.Nano Lett.21, 2642–2649.

[102] [102] Niu H S, Li H, Zhang Q C, Kim E S, Kim N Y and Li Y. 2024. Intuition-and-tactile bimodal sensing based on artificial-intelligence-motivated all-fabric bionic electronic skin for intelligent material perception.Small20, 2308127.

[103] [103] Ouyang B S, Wang J L, Zeng G, Yan J M, Zhou Y, Jiang X X, Shao B J and Chai Y. 2024. Bioinspired in-sensor spectral adaptation for perceiving spectrally distinctive features.Nat. Electron.7, 705–713.

[104] [104] Chen J X and Xu W T. 2023. 2D-materials-based optoelectronic synapses for neuromorphic applications.eScience3, 100178.

[105] [105] Liu P R, Lu L, Zhang J Y, Huo T T, Liu S X and Ye Z W. 2021. Application of artificial intelligence in medicine: an overview.Curr. Med. Sci.41, 1105–1115.

[106] [106] Singh A, Sharma A, Ahmed A, Sundramoorthy A K, Furukawa H, Arya S and Khosla A. 2021. Recent advances in electrochemical biosensors: applications, challenges, and future scope.Biosensors11, 336.

[107] [107] umak B, Brdnik S and Punik M. 2021. Sensors and artificial intelligence methods and algorithms for human–computer intelligent interaction: a systematic mapping study.Sensors22, 20.

[108] [108] Sun Z D, Zhu M L and Lee C. 2021. Progress in the triboelectric human–machine interfaces (HMIs)-moving from smart gloves to AI/haptic enabled HMI in the 5G/IoT era.Nanoenergy Adv.1, 81–120.

[109] [109] De Fazio R, Mastronardi V M, Petruzzi M, De Vittorio M and Visconti P. 2022. Human–machine interaction through advanced haptic sensors: a piezoelectric sensory glove with edge machine learning for gesture and object recognition.Future Internet15, 14.

[110] [110] Tan C H, Tan K C and Tay A. 2011. Dynamic game difficulty scaling using adaptive behavior-based AI.IEEE Trans. Comput. Intell. AI Games3, 289–301.

[111] [111] Blasch E, Pham T, Chong C Y, Koch W, Leung H, Braines D and Abdelzaher T. 2021. Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges.IEEE Aerosp. Electron. Syst. Mag.36, 80–93.

[112] [112] Qu S D, Sun L, Zhang S, Liu J Q, Li Y, Liu J C and Xu W T. 2023. An artificially-intelligent cornea with tactile sensation enables sensory expansion and interaction.Nat. Commun.14, 7181.

[113] [113] Kern N, Paulus L, Grebner T, Janoudi V and Waldschmidt C. 2023. Radar-based gesture recognition under ego-motion for automotive applications.IEEE Trans. Radar Syst.1, 542–552.

[114] [114] Wang J, Wang C C, Yin D Y, Gao Q H, Liu X K and Pan M. 2022. Cross-scenario device-free gesture recognition based on self-adaptive adversarial learning.IEEE Internet Things J.9, 7080–7090.

[115] [115] Lu Y J, Tian H, Cheng J, Zhu F, Liu B, Wei S S, Ji L H and Wang Z L. 2022. Decoding lip language using triboelectric sensors with deep learning.Nat. Commun.13, 1401.

[116] [116] Guo X G, He T Y, Zhang Z X, Luo A X, Wang F, Ng E J, Zhu Y, Liu H C and Lee C. 2021. Artificial intelligence-enabled caregiving walking stick powered by ultra-low-frequency human motion.ACS Nano15, 19054–19069.

[117] [117] Wang T H, Jin T, Lin W Y, Lin Y Q, Liu H F, Yue T, Tian Y Z, Li L, Zhang Q and Lee C. 2024. Multimodal sensors enabled autonomous soft robotic system with self-adaptive manipulation.ACS Nano18, 9980–9996.

[118] [118] Li L et al. 2024. Adaptative machine vision with microsecondlevel accurate perception beyond human retina.Nat. Commun.15, 6261.

[119] [119] Fang H, Guo J J and Wu H. 2022. Wearable triboelectric devices for haptic perception and VR/AR applications.Nano Energy96, 107112.

[120] [120] Xie A R, Li C, Chou C H, Li T, Dai C Y and Lan N. 2024. A hybrid sensory feedback system for thermal nociceptive warning and protection in prosthetic hand.Front. Neurosci.18, 1351348.

[121] [121] Nurputra D K, Kusumaatmaja A, Hakim M S, Hidayat S N, Julian T, Sumanto B, Mahendradhata Y, Saktiawati A M I, Wasisto H S and Triyana K. 2022. Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.npj Digit. Med.5, 115.

[122] [122] Chamola V, Hassija V, Gupta V and Guizani M. 2020. A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact.IEEE Access8, 90225–90265.

[123] [123] Kim K K, Ha I, Kim M, Choi J, Won P, Jo S and Ko S H. 2020. A deep-learned skin sensor decoding the epicentral human motions.Nat. Commun.11, 2149.

[124] [124] Li J Y et al. 2024. Design of AI-enhanced and hardware-supported multimodal E-skin for environmental object recognition and wireless toxic gas alarm.Nano-Micro Lett.16, 256.

[125] [125] Jiang C P, Xu H H, Yang L, Liu J Q, Li Y, Takei K and Xu W T. 2024. Neuromorphic antennal sensory system.Nat. Commun.15, 2109.

[126] [126] Narkhede P, Walambe R, Mandaokar S, Chandel P, Kotecha K and Ghinea G. 2021. Gas detection and identification using multimodal artificial intelligence based sensor fusion.Appl. Syst. Innov.4, 3.

[127] [127] Chen Y H and Sawan M. 2021. Trends and challenges of wearable multimodal technologies for stroke risk prediction.Sensors21, 460.

[128] [128] Gu Q H, Jiang S, Lian M J and Lu C W. 2018. Health and safety situation awareness model and emergency management based on multi-sensor signal fusion.IEEE Access7, 958–968.

[129] [129] Lamsal R and Kumar T V V. 2020. Artificial intelligence and early warning systems.AI and Robotics in Disaster Studies(eds Kumar T V V and Sud K). (Palgrave Macmillan, Singapore). pp 13–32.

[130] [130] Wan C J, Cai P Q, Wang M, Qian Y, Huang W and Chen X D. 2020. Artificial sensory memory.Adv. Mater.32, 1902434.

[131] [131] Mathews Z, Badia S B I and Verschure P F M J. 2012. PASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems.Inf. Sci.186, 1–19.

[132] [132] Shi Y, Gong F R, Wang M Y, Liu J J, Wu Y N and Men H. 2019. A deep feature mining method of electronic nose sensor data for identifying beer olfactory information.J. Food Eng.263, 437–445.

[133] [133] Jiang C, Jiang C C, Chen D W and Hu F. 2022. Densely connected neural networks for nonlinear regression.Entropy24, 876.

[134] [134] Gupta A, Gupta A and Gupta R 2023. Low-cost artificial intelligence enhanced hardware design for data augmentation.In Proceedings of 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering. (IEEE, Tenerife, Canary Islands, Spain). pp 1–4.

[135] [135] Zhang L, Liu H, Yang X L, Jiang Y and Wu Z Q. 2021. Intelligent denoising-aided deep learning modulation recognition with cyclic spectrum features for higher accuracy.IEEE Trans. Aerosp. Electron. Syst.57, 3749–3757.

[136] [136] Guo Y F, Liao W X, Wang Q L, Yu L X, Ji T X and Li P. 2018. Multidimensional time series anomaly detection: a GRU-based gaussian mixture variational autoencoder approach.In Proceedings of the 10th Asian Conference on Machine Learning. (PMLR, Beijing, China). pp 97–112.

[137] [137] Zoghlami N and Lachiri Z. 2012. Application of perceptual filtering models to noisy speech signals enhancement.J. Electr. Comput. Eng.2012, 282019.

[138] [138] Wu Z K, Cao L B, Zhang Q, Zhou J X and Chen H. 2024. Weakly augmented variational autoencoder in time series anomaly detection. (arXiv: 2401.03341).

[139] [139] Chen G L. 2020. Efficient, geometrically adaptive techniques for multiscale gaussian-kernel SVM classification.In Advanced Studies in Classification and Data Science(eds Imaizumi T, Okada A, Miyamoto S, Sakaori F, Yamamoto Y and Vichi M). (Springer, Singapore). pp 45–56.

[140] [140] Feng C and Liao S Z. 2017. Scalable Gaussian kernel support vector machines with sublinear training time complexity.Inf. Sci.418-419, 480–494.

[141] [141] Wang Y et al. 2021. MXene-ZnO memristor for multimodal in-sensor computing.Adv. Funct. Mater.31, 2100144.

[142] [142] Bi Y G, Zhou M, Hu Z Q, Zhang S T and Lyu G F 2022. Dynamic interaction learning and multimodal representation for drug response prediction.bioRxiv.

[143] [143] Gandhi A, Sharma A, Biswas A and Deshmukh O. 2016. GeThR-Net: a generalized temporally hybrid recurrent neural network for multimodal information fusion.In Proceedings of the Computer Vision–ECCV 2016 Workshops. (Springer, Amsterdam, The Netherlands). pp 883–899.

[144] [144] Ahmadi A and Tani J. 2019. A novel predictive-codinginspired variational RNN model for online prediction and recognition.Neural Comput.31, 2025–2074.

[145] [145] Girin L, Leglaive S, Bie X Y, Diard J, Hueber T and Alameda-Pineda X. 2020. Dynamical variational autoencoders: a comprehensive review. (arXiv: 2008.12595).

[146] [146] Kim S, Lee Y, Kim H D and Choi S J. 2020. A tactile sensor system with sensory neurons and a perceptual synaptic network based on semivolatile carbon nanotube transistors.NPG Asia Mater.12, 76.

[147] [147] Kursun O and Patooghy A. 2020. An embedded system for collection and real-time classification of a tactile dataset.IEEE Access8, 97462–97473.

[148] [148] Rasouli M, Chen Y, Basu A, Kukreja S L and Thakor N V. 2018. An extreme learning machine-based neuromorphic tactile sensing system for texture recognition.IEEE Trans. Biomed. Circuits Syst.12, 313–325.

[149] [149] Friedl K E, Voelker A R, Peer A and Eliasmith C. 2016. Human-inspired neurorobotic system for classifying surface textures by touch.IEEE Robot. Autom. Lett.1, 516–523.

[150] [150] Shi Y B, Liu C N, Wang R X, Zhou Y S, Zhang Y W and Xiu D B. 2014. Research of data mining and network coverage optimization in early warning model of chlorine gas monitoring wireless sensor network.In Proceedings of the International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014. (IET, Hsinchu, China). pp 298–304.

[151] [151] Mancini A, Cosoli G, Mobili A, Violini L, Pandarese G, Galdelli A, Narang G, Blasi E, Tittarelli F and Revel G M. 2024. A monitoring platform based on electrical impedance and AI techniques to enhance the resilience of the built environment.Acta Imeko13, 1–12.

[152] [152] Zhang L, Zhang D, Yin X and Liu Y. 2016. A novel semisupervised learning approach in artificial olfaction for E-nose application.IEEE Sens. J.16, 4919–4931.

[153] [153] Song H W, Moon D, Won Y, Cha Y K, Yoo J, Park T H and Oh J H. 2024. A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices.Sci. Adv.10, eadl2882.

[154] [154] Park D, Hoshi Y and Kemp C C. 2018. A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder.IEEE Robot. Autom. Lett.3, 1544–1551.

[155] [155] Rashid H A, Ovi P R, Busart C, Gangopadhyay A and Mohsenin T. 2022. TinyM2net: a flexible system algorithm co-designed multimodal learning framework for tiny devices. (arXiv: 2202.0430).

[156] [156] Mathews Z, Badia S B I and Verschure P F M J. 2010. Action-planning and execution from multimodal cues: an integrated cognitive model for artificial autonomous systems.In Intelligent Systems: From Theory to Practice(eds Sgurev V, Hadjiski M and Kacprzyk J). (Springer, Berlin, Heidelberg). pp 479–497.

[157] [157] Jeon G, Anisetti M, Damiani E and Kantarci B. 2020. Artificial intelligence in deep learning algorithms for multimedia analysis.Multimed. Tools Appl.79, 34129–34139.

[158] [158] Kursun O and Favorov O V. 2019. Suitability of features of deep convolutional neural networks for modeling somatosensory information processing.Proc. SPIE10995, 94–105.

[159] [159] Nedelkoski S, Cardoso J and Kao O. 2019. Anomaly detection from system tracing data using multimodal deep learning.In Proceedings of 2019 IEEE 12th International Conference on Cloud Computing. (IEEE, Milan, Italy). pp 179–186.

Tools

Get Citation

Copy Citation Text

Tian Changyu, Cho Youngwook, Song Youngho, Park Seongcheol, Kim Inho, Cho Soo-Yeon. Integration of AI with artificial sensory systems for multidimensional intelligent augmentation[J]. International Journal of Extreme Manufacturing, 2025, 7(4): 42002

Download Citation

EndNote(RIS)BibTexPlain Text
Save article for my favorites
Paper Information

Category: Topical Review

Received: Sep. 30, 2024

Accepted: Sep. 9, 2025

Published Online: Sep. 9, 2025

The Author Email:

DOI:10.1088/2631-7990/adbd98

Topics