Intelligent Healthcare Provided by Nano-Enhanced Biosensing Systems: Progress in COVID-19 Management via the Artificial Neural Network Approach

Biosensors using opto electronics mechanisms are evolving as efficient (sensitive and selective) and low-cost analytical diagnostic devices for early-stage disease diagnosis, which is crucial for person-centered health and wellness management. Due to advancements in nanotechnology in the areas of sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at the point-of-care (POC), personalized diagnostics are now possible, allowing doctors to tailor tests to each patient's unique disease profile and management requirements. Innovative biosensing technology is being pushed as the diagnostic tool of the future because of its potential to provide accurate results without requiring intrusive procedures. Because of this, this visionary piece of writing explores analytical methods for managing personalised health care that IP 203.8 109.10 On: Th , 16 F b 2023 14 53 21 can enhance the health of the general population. The end goal is to take control of a healthier tomorrow as Copyright: Ame can Scientific Pub shers soon as possible. Right now, the most crucial part of controllig the COVID-19 pandemic, a potentially fatal Delive ed by Ingenta respiratory viral disease, is the rapid, specific, and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-CoV-2) protein.

Covid-19 Interior Security Tracking System Based on the Artificial Intelligence

This article presents an inexpensive artificial intelligence solution aimed at increasing indoor safety of COVID-19, including a number of important aspects: (1) breakdown of the process (2) Method for mask identification (3). Assessment methodology of social distancing The Arduino Uno sensor system uses an infrasound sensor or heat camera, whereas the Raspberry Pi is equipped with computer vision technologies for mask detection and social distance checks. Indoor measures are the most prevalent—people with a high body heat should stay at home, masks should be worn, and their distance should be at least 1.5–2 m. In the first case, the Arduino Uno temperature sensor board is utilized, while we utilize a single-board Pi Raspberry computer coupled with camera for two additional situations, using computer vision techniques. Due to their compact size and cost, we chose to utilize these devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.