[1] [1] A. Moin, A. Zhou, A. Rahimi, A. Menon, S. Benatti et al., A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition. Nat. Electron. 4, 54 (2020). 10.1038/s41928-020-00510-8

[2] [2] M. Wang, Z. Yan, T. Wang, P. Cai, S. Gao et al., Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors. Nat. Electron. 3, 563 (2020). 10.1038/s41928-020-0422-z

[3] [3] Z. Sun, M. Zhu, X. Shan, C. Lee, Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. Nat. Commun. 13, 5224 (2022). 10.1038/s41467-022-32745-8

[4] [4] X. Liang, H. Li, W. Wang, Y. Liu, R. Ghannam et al., Fusion of wearable and contactless sensors for intelligent gesture recognition. Adv. Intell. Syst. 1, 1900088 (2019). 10.1002/aisy.201900088

[5] [5] T. Watanabe, Md. Maniruzzaman, Md.A.M. Hasan, H.-S. Lee, S.-W. Jang et al., 2D camera-based air-writing recognition using hand pose estimation and hybrid deep learning model. Electronics 12, 995 (2023). 10.3390/electronics12040995

[6] [6] S. Ahmed, W. Kim, J. Park, S.H. Cho, Radar-based air-writing gesture recognition using a novel multistream cnn approach. IEEE Internet Things J. 9, 23869 (2022). 10.1109/JIOT.2022.3189395

[7] [7] Y. Guo, F. Yin, Y. Li, G. Shen, J. Lee, Incorporating wireless strategies to wearable devices enabled by a photocurable hydrogel for monitoring pressure information. Adv. Mater. 35, 2300855 (2023). 10.1002/adma.202300855

[8] [8] W. Yue, E.-S. Kim, E. Ganbold, B.-H. Zhu, B. Oh et al., A miniature and reusable radiofrequency biosensor combining microfluidic and integrated passive technology for glucose detection. Sens. Actuators B Chem. 392, 134108 (2023). 10.1016/j.snb.2023.134108

[9] [9] Y. Li, Q. Lin, T. Sun, M. Qin, W. Yue, S. Gao, A perceptual and interactive integration strategy toward telemedicine healthcare based on electroluminescent display and triboelectric sensing 3D stacked device. Adv. Funct. Mater. (2024). 10.1002/adfm.202402356

[10] [10] H. Niu, H. Li, S. Gao, Y. Li, X. Wei et al., Perception-to-cognition tactile sensing based on artificial-intelligence-motivated human full-skin bionic electronic skin. Adv. Mater. 34, 2202622 (2022). 10.1002/adma.202202622

[11] [11] Y. Liu, C. Yiu, Z. Song, Y. Huang, K. Yao et al., Electronic skin as wireless human-machine interfaces for robotic VR. Sci. Adv. 8, eabl6700 (2022). 10.1126/sciadv.abl6700

[12] [12] J. Huang, R. Zhou, Z. Chen, Y. Wang, Z. Li et al., Highly stable and reliable capacitive strain sensor for wearable electronics based on anti-dry hydrogel electrode. Mater. Today Phys. 35, 101123 (2023). 10.1016/j.mtphys.2023.101123

[13] [13] K.K. Kim, I. Ha, M. Kim, J. Choi, P. Won et al., A deep-learned skin sensor decoding the epicentral human motions. Nat. Commun. 11, 2149 (2020). 10.1038/s41467-020-16040-y

[14] [14] Z. Zhou, K. Chen, X. Li, S. Zhang, Y. Wu et al., Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays. Nat. Electron. 3, 571 (2020). 10.1038/s41928-020-0428-6

[15] [15] T. Wang, Y. Zhao, Q. Wang, A flexible iontronic capacitive sensing array for hand gesture recognition using deep convolutional neural networks. Soft Robot. 10, 443 (2023). 10.1089/soro.2021.0209

[16] [16] P. Tan, X. Han, Y. Zou, X. Qu, J. Xue et al., Self-powered gesture recognition wristband enabled by machine learning for full keyboard and multicommand input. Adv. Mater. 34, 2200793 (2022). 10.1002/adma.202200793

[17] [17] H. Fang, L. Wang, Z. Fu, L. Xu, W. Guo et al., Anatomically designed triboelectric wristbands with adaptive accelerated learning for human–machine interfaces. Adv. Sci. 10, 2205960 (2023). 10.1002/advs.202205960

[18] [18] X. Le, Q. Shi, Z. Sun, J. Xie, C. Lee, Noncontact human–machine interface using complementary information fusion based on MEMS and triboelectric sensors. Adv. Sci. 9, 2201056 (2022). 10.1002/advs.202201056

[19] [19] A. Tashakori, Z. Jiang, A. Servati, S. Soltanian, H. Narayana et al., Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves. Nat. Mach. Intell. 6, 106 (2024). 10.1038/s42256-023-00780-9

[20] [20] J. Park, D. Kang, H. Chae, S.K. Ghosh, C. Jeong et al., Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface. Sci. Adv. 8, eabj9220 (2022). 10.1126/sciadv.abj9220

[21] [21] K.K. Kim, M. Kim, K. Pyun, J. Kim, J. Min et al., A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. Nat. Electron. 6, 64 (2023). 10.1038/s41928-022-00888-7

[22] [22] Q. He, Z. Feng, X. Wang, Y. Wu, J. Yang, A smart pen based on triboelectric effects for handwriting pattern tracking and biometric identification. ACS Appl. Mater. Interfaces 14, 49295 (2022). 10.1021/acsami.2c13714

[23] [23] H. Lee, S. Lee, J. Kim, H. Jung, K.J. Yoon et al., Stretchable array electromyography sensor with graph neural network for static and dynamic gestures recognition system. npj Flex. Electron. 7, 20 (2023). 10.1038/s41528-023-00246-3

[24] [24] L. Cui, C. Hu, W. Wang, J. Zheng, Z. Zhu et al., An adhesive, stretchable, and freeze-resistant conductive hydrogel strain sensor for handwriting recognition and depth motion monitoring. J. Colloid Interface Sci. 677, 273 (2024). 10.1016/j.jcis.2024.07.214

[25] [25] K.R. Pyun, K. Kwon, M.J. Yoo, K.K. Kim, D. Gong et al., Machine-learned wearable sensors for real-time hand motion recognition: toward practical applications in reality. Natl. Sci. Rev. 11, nwad298 (2024). 10.1093/nsr/nwad298

[26] [26] X. Wei, H. Li, W. Yue, S. Gao, Z. Chen et al., A high-accuracy, real-time, intelligent material perception system with a machine-learning-motivated pressure-sensitive electronic skin. Matter 5, 1481 (2022). 10.1016/j.matt.2022.02.016

[27] [27] Y. Li, Z. Qiu, H. Kan, Y. Yang, J. Liu et al., A human-computer interaction strategy for an FPGA platform boosted integrated “perception-memory” system based on electronic tattoos and memristors. Adv. Sci. (2024). 10.1002/advs.202402582

[28] [28] H. Niu, F. Yin, E. Kim, W. Wang, D. Yoon et al., Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence. InfoMat 5, e12412 (2023). 10.1002/inf2.12412

[29] [29] L. Jin, Z. Li, Z. Liu, B. Richardson, Y. Zheng et al., Flexible unimodal strain sensors for human motion detection and differentiation. npj Flex. Electron. 6, 74 (2022). 10.1038/s41528-022-00205-4

[30] [30] W. Yang, H. Kan, G. Shen, Y. Li, A network intrusion detection system with broadband WO3-x/WO3-x-Ag/WO3-x optoelectronic memristor. Adv. Funct. Mater. 34, 2312885 (2024). 10.1002/adfm.202312885

[31] [31] T. Kim, Y. Shin, K. Kang, K. Kim, G. Kim et al., Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces. Nat. Commun. 13, 5815 (2022). 10.1038/s41467-022-33457-9

[32] [32] H. Zhang, H. Li, Y. Li, Biomimetic electronic skin for robots aiming at superior dynamic-static perception and material cognition based on triboelectric-piezoresistive effects. Nano Lett. 24, 4002 (2024). 10.1021/acs.nanolett.4c00623

[33] [33] F. Wen, Z. Zhang, T. He, C. Lee, AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove. Nat. Commun. 12, 5378 (2021). 10.1038/s41467-021-25637-w

[34] [34] M.L. Hammock, A. Chortos, B.C.-K. Tee, J.B.-H. Tok, Z. Bao, 25th Anniversary article: the evolution of electronic skin (e-skin): a brief history, design considerations, and recent progress. Adv. Mater. 25, 5997 (2013). 10.1002/adma.201302240

[35] [35] T. Chen, S. Kornblith, M. Norouzi, G. Hinton, A simple framework for contrastive learning of visual representations, in Proceedings of the 37th International Conference on Machine Learning (PMLR) (2020), pp. 1597–1607

[36] [36] J.-B. Grill, F. Strub, F. Altché, C. Tallec, P.H. Richemond et al., Bootstrap your own latent-a new approach to self-supervised learning. arXiv, 2006.07733.

[37] [37] E. Eldele, M. Ragab, Z. Chen, M. Wu, C.-K. Kwoh et al., Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Trans. Pattern Anal. Mach. Intell. 45, 15604 (2023). 10.1109/TPAMI.2023.3308189

[38] [38] B.K. Iwana, S. Uchida, Time series data augmentation for neural networks by time warping with a discriminative teacher, in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy (2021), pp. 3558–3565

[39] [39] M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski et al., Unsupervised learning of visual features by contrasting cluster assignments, in Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (2020), pp. 9912–9924

[40] [40] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: pre-training of deep bidirectional transformers for language understanding. arXiv, 1810.04805 (2018).

[41] [41] Y. Chang, L. Wang, R. Li, Z. Zhang, Q. Wang et al., First decade of interfacial iontronic sensing: from droplet sensors to artificial skins. Adv. Mater. 33, 2003464 (2021). 10.1002/adma.202003464

[42] [42] J. Shi, Y. Dai, Y. Cheng, S. Xie, G. Li et al., Embedment of sensing elements for robust, highly sensitive, and cross-talk-free iontronic skins for robotics applications. Sci. Adv. 9, eadf8831 (2023). 10.1126/sciadv.adf8831

[43] [43] W. Cheng, J. Wang, Z. Ma, K. Yan, Y. Wang et al., Flexible pressure sensor with high sensitivity and low hysteresis based on a hierarchically microstructured electrode. IEEE Electron Device Lett. 39, 288 (2018). 10.1109/LED.2017.2784538

[44] [44] B. Ji, Q. Zhou, M. Lei, S. Ding, Q. Song et al., Gradient architecture-enabled capacitive tactile sensor with high sensitivity and ultrabroad linearity range. Small 17, 2103312 (2021). 10.1002/smll.202103312

[45] [45] Z. Wang, W. Yan, T. Oates, Time series classification from scratch with deep neural networks: a strong baseline, in 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA (2017), pp. 1578–1585.

[46] [46] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is all you need, in Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, United States (2017)

[47] [47] Q. Wang, B. Li, T. Xiao, J. Zhu, C. Li et al., Learning deep transformer models for machine translation, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy (2019), pp. 1810–1822.

[48] [48] G.S. Dhillon, P. Chaudhari, A. Ravichandran, S. Soatto, A baseline for few-shot image classification. arXiv, 1909.02729 (2019).

[49] [49] W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, J.-B. Huang, A closer look at few-shot classification. arXiv, 1904.04232 (2019).

[50] [50] J. Soni, N. Prabakar, H. Upadhyay, Visualizing high-dimensional data using t-distributed stochastic neighbor embedding algorithm, in Principles of Data Science (Springer, Cham, 2020), pp. 189–206.