[1] Ren, H., Anicic, D., Li, X., Runkler, T.: On-device online learning and semantic management of TinyML systems. ACM Trans. Embedded Comput. Syst. 23(4), 1–32 (2024)

[2] Heydari, S., Mahmoud, Q.H.: Tiny machine learning and on-device inference: a survey of applications, challenges, and future directions. Sensors 25(10), 3191 (2025)

[3] Ben Dhiab, Y., Hizem, M., Karmous, N., Ould-Elhassen Aoueileyine, M., Bouallegue, R.: An IoT-based multimodal wearable framework for real-time epileptic seizures detection using TinyML. In: International Conference on Advanced Information Networking and Applications, pp. 70–80. Springer (2025)

[4] Zaidi, S.A.R., Hayajneh, A.M., Hafeez, M., Ahmed, Q.Z.: Unlocking edge intelligence through tiny machine learning (TinyML). IEEE Access 10, 100867–100877 (2022)

[5] Sanchez-Iborra, R.: LPWAN and embedded machine learning as enablers for the next generation of wearable devices. Sensors 21(15), 5218 (2021)

[6] Nguyen, T.V., Phung, T.N., Do, D.C.: A bibliometric and thematic analysis of systematic reviews of artificial intelligence in education. In: International Conference on Advances in Information and Communication Technology, pp. 337–351. Springer (2024)

[7] Lamaakal, I., El Makkaoui, K., Ouahbi, I., Maleh, Y.: A TinyML model for gesture-based air handwriting Arabic numbers recognition. Procedia Comput. Sci. 236, 589–596 (2024)

[8] Chen, Z., et al.: Augmenting embodied learning in welding training: the co-design of an AR- and TinyML-enabled welding system. In: Proceedings of the 18th International Conference on Tangible, Embedded, and Embodied Interaction, pp. 1–14. ACM (2024)

[9] Gragnaniello, M., Marrazzo, V.R., Borghese, A., Maresca, L., Breglio, G., Riccio, M.: Edge-AI enabled wearable device for noninvasive type 1 diabetes detection using ECG signals. Bioengineering 12(1), 4 (2024)

[10] Mai, N.D., Nguyen, H.T., Chung, W.Y.: Real-time on-chip machine-learning-based wearable behind-the-ear electroencephalogram device for emotion recognition. IEEE Access 11, 47258–47271 (2023)

[11] Arya, S., Kaji, A.H., Boermeester, M.A.: PRISMA reporting guidelines for meta-analyses and systematic reviews. JAMA Surg. 156(8), 789–790 (2021)

[12] Nguyen, T.V., Jung, C.T.H., Yooc, S.C., Jung, K.: Unveiling augmented reality applications: exploring influential factors through comprehensive review. SN Comput. Sci. 4(5), 694 (2023)

[13] Nguyen, K.S., Nguyen, T.V., Ngo, H.H., Nguyen, D.B.: Application of large language models in geographic map analysis and visualization. In: International Conference on Advances in Information and Communication Technology, pp. 127–136. Springer (2024)

[14] Signoretti, G., Silva, M., Andrade, P., Silva, I., Sisinni, E., Ferrari, P.: An evolving TinyML compression algorithm for IoT environments based on data eccentricity. Sensors 21(12), 4153 (2021)

[15] Andrade, P., Silva, I., Silva, M., Flores, T., Cassiano, J., Costa, D.G.: A TinyML soft-sensor approach for low-cost detection and monitoring of vehicular emissions. Sensors 22(10), 3838 (2022)

[16] Hou, K.M., Diao, X., Shi, H., Ding, H., Zhou, H., de Vaulx, C.: Trends and challenges in AIoT/IIoT/IoT implementation. Sensors 23(11), 5074 (2023)

[17] Tazarv, A., Labbaf, S., Reich, S.M., Dutt, N., Rahmani, A.M., Levorato, M.: Personalized stress monitoring using wearable sensors in everyday settings. In: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 7332–7335. IEEE (2021)

[18] Huang, D.M., Huang, J., Qiao, K., Zhong, N.S., Lu, H.Z., Wang, W.J.: Deep learning-based lung sound analysis for intelligent stethoscope. Mil. Med. Res. 10(1), 44 (2023)

[19] Nguyen, T.V., Phung, T.N.: Enhanced literature review visualization: a novel sorted stream graphs with integrated word elements. In: International Conference on Advances in Information and Communication Technology, pp. 159–168. Springer (2024)