Internet of things (IoT) based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care

• Published in: IEEE internet of things journal Vol. 10; no. 21; p. 1
• Main Authors: Balasundaram, A., Routray, Sidheswar, Prabu, A V, Krishnan, Prabhakar, Malla, Prince Priya, Maiti, Moinak
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Anomalies; Body parts; Data processing; Data transmission; Emergency medical care; Emergency medical services; Health care; Health services; Industries; Internet of medical things; Internet of Things; IoT; LSTM; Mathematical models; Medical Data Analytics; Medical diagnostic imaging; Medical services; Monitoring; Parameters; Patients; Physicians; Prediction algorithms; Smart Healthcare; U-Net
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3246065

The Internet of Things has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases such as accidents, heart attacks, etc. Only the patient's vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients' lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, Multi-Model IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.