AI-Enabled Smart Jacket : Revolutionizing Healthcare Monitoring and Environmental Control

M. Harini; K. S. Roshni; R. Nega; Dr. D. Nithya

doi:10.32628/CSEIT24102132

Authors

M. Harini UG Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India Author
K. S. Roshni UG Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India Author
R. Nega UG Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India Author
Dr. D. Nithya Assistant Professor, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India Author

DOI:

https://doi.org/10.32628/CSEIT24102132

Keywords:

AI, Smart Jacket, Healthcare Monitoring, Environmental Control, Deep Q-Learning, Deep Belief Networks, Wearable Technology

Abstract

The AI smart jacket represents a groundbreaking innovation in healthcare monitoring, merging wearable technology, advanced sensors, and machine learning algorithms. This wearable device integrates GPS and biometric sensors for real-time data capture, facilitating remote access to health metrics via wireless networking. With a focus on user comfort and privacy, the jacket features machine-washable fabrics and robust security measures. Its array of functionalities includes temperature adjustment, SOS capability, oximeter, ECG monitoring, pulse rate tracking, and a fan with massage functions. The jacket's versatility extends from chronic illness management to elderly care, promising to enhance overall well-being and quality of life. By seamlessly integrating technology into daily life, the AI smart jacket stands as a pioneering solution at the forefront of healthcare innovation.

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References

Subhan, F., Mirza, A., Su’ud, M.B.M., Alam, M.M., Nisar, S., Habib, U. and Iqbal, M.Z., 2023. AI-enabled wearable medical internet of things in healthcare system: A survey. Applied Sciences, 13(3), p.1394. DOI: https://doi.org/10.3390/app13031394

Ramakrishnan, S., Jijitha, S. and Amudha, T., 2024. Roadmap of AI and IoMT in Smart Healthcare: Current Applications and Future Perspectives. Internet of Medical Things in Smart Healthcare, pp.137-161. DOI: https://doi.org/10.1201/9781003369035-8

Batra, P. and Dave, D.M., 2024. Revolutionizing Healthcare Platforms: The Impact of AI on Patient Engagement and Treatment Efficacy. International Journal of Science and Research (IJSR), 13(10.21275), pp.613-624.

Arthi, K. and Chidhambararajan, B., 2024. 1 Advanced Enabling Technologies of IoMT in Personalized Healthcare. Technological Advancement in Internet of Medical Things and Blockchain for Personalized Healthcare: Applications and Use Cases. DOI: https://doi.org/10.1201/9781003405450-1

Verma, D., Singh, K.R., Yadav, A.K., Nayak, V., Singh, J., Solanki, P.R. and Singh, R.P., 2022. Internet of things (IoT) in nano-integrated wearable biosensor devices for healthcare applications. Biosensors and Bioelectronics: X, 11, p.100153. DOI: https://doi.org/10.1016/j.biosx.2022.100153

Htet, A., Liana, S.R., Aung, T. and Bhaumik, A., 2024. Intelligent Engineering and Applied Sciences for Comprehensive Environmental Sustainability: An Interdisciplinary and Cross-Sectoral Approach. In Fostering Cross-Industry Sustainability With Intelligent Technologies (pp. 183-210). IGI Global. DOI: https://doi.org/10.4018/979-8-3693-1638-2.ch012

Rogdakis, K., Psaltakis, G., Fagas, G., Quinn, A., Martins, R. and Kymakis, E., 2024. Hybrid chips to enable a sustainable internet of things technology: opportunities and challenges. Discover Materials, 4(1), p.4. DOI: https://doi.org/10.1007/s43939-024-00074-w

Chen, J., Yi, C., Okegbile, S.D., Cai, J. and Shen, X.S., 2023. Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Communications Surveys & Tutorials. DOI: https://doi.org/10.1109/COMST.2023.3308717

Awad, A., Trenfield, S.J., Pollard, T.D., Ong, J.J., Elbadawi, M., McCoubrey, L.E., Goyanes, A., Gaisford, S. and Basit, A.W., 2021. Connected healthcare: Improving patient care using digital health technologies. Advanced Drug Delivery Reviews, 178, p.113958. DOI: https://doi.org/10.1016/j.addr.2021.113958

Rajaraman, S., 2024. Artificial Intelligence in Neuro Degenerative Diseases: Opportunities and Challenges. AI and Neuro-Degenerative Diseases: Insights and Solutions, pp.133-153. DOI: https://doi.org/10.1007/978-3-031-53148-4_8

Rao, S.S., Fernandes, S.L., Singh, C. and Gatti, R.R. eds., 2023. AIoT and Big Data Analytics for Smart Healthcare Applications. Bentham Science Publishers.

Grewal, D., Benoit, S., Noble, S.M., Guha, A., Ahlbom, C.P. and Nordfält, J., 2023. Leveraging In-Store Technology and AI: Increasing Customer and Employee Efficiency and Enhancing their Experiences. Journal of retailing. DOI: https://doi.org/10.1016/j.jretai.2023.10.002

Thakur, R., Baghel, M., Bhoj, S., Jamwal, S., Chandratre, G.A., Vishaal, M., Badgujar, P.C., Pandey, H.O. and Tarafdar, A., 2024. Digitalization of livestock farms through blockchain, big data, artificial intelligence, and Internet of Things. In Engineering Applications in Livestock Production (pp. 179-206). Academic Press. DOI: https://doi.org/10.1016/B978-0-323-98385-3.00012-8

Das, K. and Banerjee, J.S., 2022. Green IoT for intelligent cyber-physical systems in industry 4.0: a review of enabling technologies, and solutions. Applications of machine intelligence in engineering, pp.463-478. DOI: https://doi.org/10.1201/9781003269793-49

Verma, V. and Thakur, P., 2023. Nanotechnology in natural world and its application in medical sector. Recent Innovative Tren ds in Interdisciplinary Research Areas, 2, p.17.