2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 1 , PP: 31-45, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
S. Phani Praveen 1 * , Chandra Shikhi Kodete 2 , Saibaba velidi 3 , Srikanth Bhyrapuneni 4 , Suresh Babu Satukumati 5 , Vahiduddin Shariff 6
Doi: https://doi.org/10.54216/JISIoT.130103
Medical care conveyance has been transformed by the Internet of Things (IoT's) combination into wellbeing systems, which provides doctors and patients with continuous on-request services. However, this coordination poses questions with respect to the precision of the information and possible security risks. This research expects to present a sharp character the executives structure planned for IoT and distributed computing based personalized medical care frameworks. The purpose is to upgrade confirmation processes while restricting security threats through the double-dealing of multimodal encoded biometric features. The suggested approach incorporates biometric-based continuous authentication together with combined and concentrated personality access strategies. To safeguard patient information in the cloud, it combines electrocardiogram (ECG) and photoplethysmogram (PPG) signals for authentication, which is further bolstered by homomorphic encryption (HE). An AI (ML) model was used to assess the system's reasonability including a dataset of 20 clients in various seating configurations. The merged based biometric structure defeated standalone ECG or PPG signal-based procedures in perceiving and authenticating every client with 100% exactness. The proposed framework makes significant improvements to the privacy and security of personalized healthcare frameworks. It fulfills the essential security necessities and is by the by viable enough to run on low-end processors. It guarantees trustworthy authentication and protects against conventional security threats by utilizing multimodal biometric features and cutting-edge encryption techniques.
Healthcare , Internet of Things (IoT) , Cloud computing , Smart biometric identity management , electrocardiogram(ECG) , photoplethysmogram (PPG) , Homomorphic Encryption (HE) , Machine learning (ML)
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