AI-Powered Wearable Devices for Real-Time Health Monitoring: Technologies, Challenges, and Emerging Trends
DOI:
https://doi.org/10.38032/jea.2026.02.001Keywords:
AI-powered wearables, Real-time health monitoring, Machine learning in healthcare, Embedded systems, Predictive diagnositicsAbstract
Artificial intelligence (AI) enabled wearable devices have revolutionized the healthcare monitoring by offering real-time and continuous monitoring of users' physiological signals used at home. This review analyzed the technological, algorithmic and clinical advancement from 2020 to 2025, detailing the technologies for biosensor design, e-enabled Machine Learning (ML) solutions, edge computing architectures, and privacy-preserving frameworks. Based on the PRISMA-ScR approach, quantitative Neural Networks (NNs), AutoML pipelines, federated learning and secure on-device analytics in a representative fashion were reviewed. Significant innovations in core basic pillars were detected: frontier sensor technologies, including multimodal fusion, and self-powered systems. Resource-efficient embedded AI models customized for constrained environments have further enhanced on-device intelligence, real-time analytics, ethical norms and regulatory paradigms. Nevertheless, such challenges are still very complicated and hard to solve. Signal instability, energy constraints, algorithmic bias and lack of demographic diversity in training data undermine model reliability and generalizability, compromising the safe, fair, and scalable implementation of such AI-driven wearable health monitoring systems in real clinical settings. The lack of validation in real-world scenarios, model explainability and alignment with regulatory frameworks of most have hampered their clinical adoption and broad-scale deployment. Ethical aspects like data sovereignty, fairness, and dynamic consent are still largely to be formed. This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables. This proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.
References
[1] Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The emergence of AI-based wearable sensors for digital health technology: A review. Sensors, 23(23), 9498. DOI: https://doi.org/10.3390/s23239498
[2] Staszak, K., Tylkowski, B., & Staszak, M. (2023). From data to diagnosis: How machine learning is changing heart health monitoring. International Journal of Environmental Research and Public Health, 20(5), 4605. DOI: https://doi.org/10.3390/ijerph20054605
[3] World Health Organization (WHO). (2024). Global status report on noncommunicable diseases 2024. Geneva: WHO. Retrieved July 17, 2025.
[4] LaBoone, P. A., & Marques, O. (2024). Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights, 4(2), 100294.
[5] Aziz, W., Anwar, S. M., Majid, M., & Younis, M. S. (2021). ECG based heartbeat classification for arrhythmia detection using machine learning. Scientific Reports, 11, 18788. DOI: https://doi.org/10.1038/s41598-021-97118-5
[6] Prabakaran, B., Rajkumar, S., & Parthiban, L. (2021). BioNetExplorer: Exploration of DNN architectures for biosignal processing in wearable health devices. IEEE Internet of Things Journal, 8(15), 12542–12553. DOI: https://doi.org/10.1109/JIOT.2021.3065815
[7] Alimbayeva, Z., Alimbayev, C., Ozhikenov, K., Bayanbay, N., & Ozhikenova, A. (2024). Wearable ECG device and machine learning for heart monitoring. Sensors, 24(13), 4201. DOI: https://doi.org/10.3390/s24134201
[8] Burrello, A., Xu, Z., Zhang, W., Tagliapietra, L., Benini, L., & Atienza, D. (2021). Q PPG: A multi task quantized neural network for energy efficient PPG based heart-rate monitoring on wearable devices. IEEE Transactions on Biomedical Circuits and Systems, 15(5), 1062–1075. DOI: https://doi.org/10.1109/TBCAS.2021.3122017
[9] Gragnaniello, M., Marrazzo, V. R., Borghese, A., Maresca, L., Breglio, G., & Riccio, M. (2024). Edge-AI enabled wearable device for non-invasive type 1 diabetes detection using ECG signals. Bioengineering, 12(1), 4.
[10] Ribeiro, H. D. M., Arnold, A., Howard, J. P., Shun-Shin, M. J., Zhang, Y., Francis, D. P., ... & Zolgharni, M. (2022). ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study. Computers in Biology and Medicine, 143, 105249.
[11] De Melo Ribeiro, D. V., & de Carvalho Filho, A. O. (2022). Real time ECG arrhythmia detection using quantized deep learning on wearable devices. Computers in Biology and Medicine, 142, 105249. DOI: https://doi.org/10.1016/j.compbiomed.2022.105249
[12] Lu, H., Feng, X. & Zhang, J. (2024). Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN. BMC Med Inform Decis Mak 24, 194 https://doi.org/10.1186/s12911-024-02599-9 DOI: https://doi.org/10.1186/s12911-024-02599-9
[13] Faruque, M. A. A., Mortlock, T., & Al Faruque, M. A. (2023). Stress detection using context aware sensor fusion from wearable devices (SELF CARE ensemble ML for robust wrist/chest sensors). arXiv Preprint.
[14] Atzori, L., Lera, I., & Morabito, G. (2023). Edge AI in smart watches: Enhancing on-device cardiovascular diagnostics. Sensors, 23(15), 6896.
[15] Arikumar, K. S., Prathiba, S. B., Alazab, M., Gadekallu, T. R., Pandya, S., Khan, J. M., & Moorthy, R. S. (2022). FL-PMI: Federated learning-based person movement identification through wearable devices in smart healthcare systems. Sensors, 22(4), 1377. DOI: https://doi.org/10.3390/s22041377
[16] Zang, Y., Xue, Q., Du, Z., He, D., & Sharma, P. (2022). FRESH: A certificate less privacy preserving federated learning scheme in wearable healthcare systems. Information Sciences, 598, 92–108.
[17] He, Z., Yuan, S., Zhao, J., Du, B., Yuan, Z., Alhudhaif, A., ... & Althubiti, S. A. (2022). A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Information Sciences, 606, 649-668. DOI: https://doi.org/10.1016/j.ins.2022.05.070
[18] Rahman, M. M., & Azad, A. (2023). Federated stress detection with self-attention in wearable-based healthcare systems. Mobile Computing and Biomedical Informatics, 3(1), 1–13. DOI: https://doi.org/10.1155/2023/2075022
[19] Wei, S., & Wu, Z. (2023). The application of wearable sensors and machine learning algorithms in rehabilitation training: A systematic review. Sensors, 23(18), 7667. DOI: https://doi.org/10.3390/s23187667
[20] Saad, H.S., Zaki, J.F.W. & Abdelsalam, M.M. Employing of machine learning and wearable devices in healthcare system: tasks and challenges. Neural Comput & Applic 36, 17829–17849 (2024). DOI: https://doi.org/10.1007/s00521-024-10197-z
[21] Spathis, D., Perez Pozuelo, I., Brage, S., Wareham, N. J., & Mascolo, C. (2020). Self supervised transfer learning of physiological representations from free living wearable data. DOI: https://doi.org/10.1145/3450439.3451863
[22] Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2020). FedHome: Cloud edge based personalized federated learning for in-home health monitoring. IEEE Transactions on Mobile Computing, 21(8), 2818–2832. DOI: https://doi.org/10.1109/TMC.2020.3045266
[23] Palakurti, N. R. (2023). AI-driven personal health monitoring devices: Trends and future directions. Journal of Engineering Advancements, 3(3), 41–51.
[24] Palakurti, N. R., & Das, S. (2024). Federated learning-based stress and fatigue monitoring with wearable sensors. Journal of Engineering Advancements, 4(1), 15–28.
[25] Alzghaibi, H. (2025). Nurses’ perspectives on AI-enabled wearable health technologies: Opportunities and challenges in clinical practice. BMC Nursing, 24, 799. DOI: https://doi.org/10.1186/s12912-025-03343-y
[26] Islam, M. M., Karim, M. R., Karray, F., & Wahid, K. A. (2023). Predictis: An IoT based predictive system for cardiovascular risk assessment using machine learning. BMC Health Services Research, 23, 541. DOI: https://doi.org/10.1186/s12913-023-09104-4
[27] Zhou, H., & Chai, Y. (2020). Near sensor and in sensor computing for next generation intelligent wearables. Nature Electronics, 3(10), 664–671. DOI: https://doi.org/10.1038/s41928-020-00501-9
[28] Natarajan, R., Krishna, S., & Ranjith, C. P. (2025, February). A novel federated learning framework for healthcare applications using wearable devices. In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC) (pp. 1–6). IEEE. DOI: https://doi.org/10.1109/ICAIC63015.2025.10848974
[29] Chamon, L., Ragab, M., & Ribeiro, A. (2025). Noise driven AI sensors for secure wearable health monitoring using physical unclonable functions (PUFs). Research Square Preprint. https://doi.org/10.21203/rs.3.rs-4474411/v1 DOI: https://doi.org/10.1109/MWSCAS53549.2025.11244389
[30] Wang, W., Li, X., Qiu, X., Zhang, X., Brusic, V., & Zhao, J. (2023). A privacy-preserving framework for federated learning in smart healthcare systems. Information Processing & Management, 60(1), 103167. DOI: https://doi.org/10.1016/j.ipm.2022.103167
[31] Pu, X., Liu, M., Chen, X., Sun, J., Du, C., & Wang, Z. L. (2022). Wearable triboelectric sensors integrated with artificial intelligence for healthcare applications. iScience, 25(1), 102027. DOI: https://doi.org/10.1016/j.isci.2020.102027
[32] Shen, Y., Lin, S., He, Y., & Zhang, H. (2022). Sleep apnea detection from PPG using multi task convolutional neural networks on wearable bracelets. IEEE Internet of Things Journal, 9(15), 13264–13275. DOI: https://doi.org/10.1109/JIOT.2022.3195777
[33] Palakurti, N. R., & Ahmed, R. (2025). Real-time biosignal analysis using embedded AutoML for wearable health devices. Journal of Engineering Advancements, 5(2), 67–82.
[34] Huang, G., Chen, X., & Liao, C. (2025). AI-driven wearable bioelectronics in digital healthcare. Biosensors, 15(7), 410. DOI: https://doi.org/10.3390/bios15070410
[35] Sharma, D., & Sarkar, S. (2022). Enabling inference and training of deep learning models for AI applications on IoT edge devices. In Artificial Intelligence-Based Internet of Things Systems (pp. 267–283). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-87059-1_10
[36] Abubaker, H. A., & Babayigit, B. (2023). Detection of cardiovascular diseases in ECG images using hybrid ML/DL models. IEEE Transactions on Artificial Intelligence, 4(2), 108–117. DOI: https://doi.org/10.1109/TAI.2022.3159505
[37] Meng, F., Qiu, C., & Yuan, X. (2025). Energy efficient CNNs for on device ECG based diabetes classification. Bioengineering, 12(1), 4.
[38] Gragnaniello, M., Marrazzo, V. R., Borghese, A., Maresca, L., Breglio, G., & Riccio, M. (2024). Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals. Bioengineering, 12(1), 4. DOI: https://doi.org/10.3390/bioengineering12010004
[39] Mao, Y., Yu, X., Huang, K., Zhang, Y. J. A., & Zhang, J. (2024). Green edge AI: A contemporary survey. Proceedings of the IEEE. Advance online publication. DOI: https://doi.org/10.1109/JPROC.2024.3437365
[40] ur Rehman, M. H., Salah, K., Damiani, E., & Svetinovic, D. (2020, July). Towards blockchain-based reputation-aware federated learning. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 183–188). IEEE. DOI: https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9163027
[41] Alharbey, R. A., & Jamil, F. (2025). Federated learning framework for real-time activity and context monitoring using edge devices. Sensors, 25(4), 1266. DOI: https://doi.org/10.3390/s25041266
[42] Abiodun, T. N., Okunbor, D., & Osamor, V. C. (2022). Remote health monitoring in clinical trial using machine learning techniques: A conceptual framework. Health and Technology, 12(2), 359–364. DOI: https://doi.org/10.1007/s12553-022-00652-z
[43] Kabashkin, I. (2024). Integration of foundation models and federated learning in AIoT-based aircraft health monitoring systems. Mathematics, 12(21), 3428. DOI: https://doi.org/10.3390/math12213428
[44] Gragnaniello, M., Balbi, F., Martellotta, G., Borghese, A., Marrazzo, V. R., Maresca, L., ... & Riccio, M. (2024, June). Edge-AI on wearable devices: Myocardial infarction detection with spectrogram and 1D-CNN. In 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON) (pp. 485–490). IEEE. DOI: https://doi.org/10.1109/MELECON56669.2024.10608624
[45] Mahajan, A., Heydari, K., & Powell, D. (2025). Wearable AI to enhance patient safety and clinical decision-making. npj Digital Medicine, 8(1), 176. DOI: https://doi.org/10.1038/s41746-025-01554-w
[46] Srivastav, A. K., Das, P., & Srivastava, A. K. (2024). Biometric security systems and wearable devices. In Biotech and IoT: An Introduction Using Cloud-Driven Labs (pp. 205–283). DOI: https://doi.org/10.1007/979-8-8688-0527-1_8
[47] Kim, D., Bang, J., Jeong, J., Koo, D. L., & Ko, S. H. (2025). Intelligent soft wearable bioelectronics for neurological disorders. Materials Horizons. Advance online publication. DOI: https://doi.org/10.1039/D5MH00528K
[48] LaBoone, P. A., & Marques, O. (2024). Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights, 4(2), 100294. DOI: https://doi.org/10.1016/j.jjimei.2024.100294
[49] Bulus, B., Ibrahim, M., & Kwaha, F. I. (2026). Intelligent medical imaging systems for rapid real-time diagnosis: Deep learning, edge AI, hardware acceleration, and healthcare intelligence. Advances in Medical Sciences and Research, 1(2), 101–119. DOI: https://doi.org/10.55578/amsr.2605.008
[50] Bali, B., & Garba, E. J. (2021). Neuro-fuzzy approach for prediction of neurological disorders: A systematic review. SN Computer Science, 2(4), 307. DOI: https://doi.org/10.1007/s42979-021-00710-9
[51] Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022). Machine learning for healthcare wearable devices: The big picture. Journal of Healthcare Engineering, 2022(1), 4653923. DOI: https://doi.org/10.1155/2022/4653923
[52] Ortiz, B. L., Gupta, V., Kumar, R., Jalin, A., Cao, X., Ziegenbein, C., ... & Choi, S. W. (2024). Data preprocessing techniques for AI and machine learning readiness: Scoping review of wearable sensor data in cancer care. JMIR mHealth and uHealth, 12(1), e59587. DOI: https://doi.org/10.2196/59587
[53] Ahmed, A., Aziz, S., Abd-Alrazaq, A., Farooq, F., Househ, M., & Sheikh, J. (2023). The effectiveness of wearable devices using artificial intelligence for blood glucose level forecasting or prediction: Systematic review. Journal of Medical Internet Research, 25, e40259. DOI: https://doi.org/10.2196/40259
[54] Junaid, S. B., Imam, A. A., Abdulkarim, M., Surakat, Y. A., Balogun, A. O., Kumar, G., ... & Hashim, A. S. (2022). Recent advances in artificial intelligence and wearable sensors in healthcare delivery. Applied Sciences, 12(20), 10271. DOI: https://doi.org/10.3390/app122010271
[55] Radhakrishnan, K., Ramakrishnan, D., & Freeda, R. A. (2025). Federated Learning for Artificial Intelligence in Embedded Systems. ICCK Transactions on Emerging Topics in Artificial Intelligence, 2(2), 91-115. DOI: https://doi.org/10.62762/TETAI.2025.440076
[56] Ramesh Palakurti, N., & Khan, P. (2025). Edge AI optimization for real time anomaly detection in smart health wearables. Journal of Engineering Advancements, 5(3), 23–39.
[57] Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., ... & Band, S. S. (2023). Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Computing, 26(4), 2271–2311. DOI: https://doi.org/10.1007/s10586-022-03658-4
[58] Robinson, T., Condell, J., Ramsey, E., & Leavey, G. (2023). Self-management of subclinical common mental health disorders (anxiety, depression and sleep disorders) using wearable devices. International Journal of Environmental Research and Public Health, 20(3), 2636. DOI: https://doi.org/10.3390/ijerph20032636
[59] Shumba, A. T., Montanaro, T., Sergi, I., Bramanti, A., Ciccarelli, M., Rispoli, A., ... & Patrono, L. (2023). Wearable technologies and AI at the far edge for chronic heart failure prevention and management: A systematic review and prospects. Sensors, 23(15), 6896. DOI: https://doi.org/10.3390/s23156896
[60] Sureja, N., Mehta, K., Shah, V., & Patel, G. (2023). Machine learning in wearable healthcare devices. In Machine Learning for Advanced Functional Materials (pp. 281–303). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-99-0393-1_13
[61] Wang, W., Chen, J., Hu, Y., Liu, H., Chen, J., Gadekallu, T. R., ... & Hu, X. (2024). Integration of artificial intelligence and wearable internet of things for mental health detection. International Journal of Cognitive Computing in Engineering, 5, 307–315. DOI: https://doi.org/10.1016/j.ijcce.2024.07.002
[62] Goni, I., Bali, B., Ahmad, B. M., Duwa, B. B., & Iwendi, C. (2024). Improving telemedicine with digital twin-driven machine learning: A novel framework. Global Journal of Sciences, 1(2), 58–70.
[63] Yammouri, G., & Ait Lahcen, A. (2024). AI-reinforced wearable sensors and intelligent point-of-care tests. Journal of Personalized Medicine, 14(11), 1088. DOI: https://doi.org/10.3390/jpm14111088
Downloads
Published
Data Availability Statement
No new data were created or analyzed in this review study
Issue
Section
License
Copyright (c) 2026 Bulus Bali, Favanza Iliya Kwaha
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the articles published by this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
