An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors. (13th February 2023)

• Record Type: Journal Article
• Authors: Bhatt, Mohammed Wasim; Sharma, Sparsh
• Other Names: Azeem Irshad Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2023(2023)
• Publication Date: 2023-02-13
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2023/1066547
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.