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Implementation of Machine Learning for Smart Wearables in the Healthcare Sector

Implementation of Machine Learning for Smart Wearables in the Healthcare Sector

Harishchander Anandaram, Deepa Gupta, Ch. Indira Priyadarsini, Benita Christopher
Copyright: © 2024 |Pages: 15
ISBN13: 9798369336793|EISBN13: 9798369336809
DOI: 10.4018/979-8-3693-3679-3.ch013
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MLA

Anandaram, Harishchander, et al. "Implementation of Machine Learning for Smart Wearables in the Healthcare Sector." Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications, edited by Alex Khang, IGI Global, 2024, pp. 207-221. https://doi.org/10.4018/979-8-3693-3679-3.ch013

APA

Anandaram, H., Gupta, D., Priyadarsini, C. I., & Christopher, B. (2024). Implementation of Machine Learning for Smart Wearables in the Healthcare Sector. In A. Khang (Ed.), Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications (pp. 207-221). IGI Global. https://doi.org/10.4018/979-8-3693-3679-3.ch013

Chicago

Anandaram, Harishchander, et al. "Implementation of Machine Learning for Smart Wearables in the Healthcare Sector." In Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications, edited by Alex Khang, 207-221. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3679-3.ch013

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Abstract

Artificial intelligence (AI) and the internet of things (IoT) are two of the world's most rapidly expanding technologies. More and more people are settling in urban areas, and the notion of a “smart city” centres on improved access to high-quality medical services. An exhaustive knowledge of the different brilliant city structures is vital for carrying out IoT and man-made intelligence for remote health monitoring (RHM) frameworks. The advancements, devices, frameworks, models, plans, use cases, and software programmes that comprise the backbone of these frameworks are all essential components. Clinical decision support systems and other variants of healthcare delivery also make use of ML techniques for creating analytic representations. After each component has been thoroughly examined, clinical decision support systems provide personalized recommendations for therapy, lifestyle changes, and care plans to patients. Medical care applications benefit from wearable innovation's ability to monitor and analyse data from the user's activities, temperature, heart rate, blood sugar, etc.

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