Safety issues in health is to be considered as primary necessity. Fear causes the changes in the activity of the nervous system that influences various body parameters. Many health problems are diagnosed by physiological and chemical changes in the human body. Consecutive fear feeling can mislead to unconsciousness. Thus, it causes a threatened feeling and people become insecure. People experiencing prolonged fear makes them disconnected and separated from others creating a dissonance in the world. Especially when kids suffer being a victim unable to communicate to parent can be recognized by monitoring child’s fear activity. This paper suggests a smart protection system under a prolonged fearfulness. The paper proposes of using a finger vein sensing with the advantage of image processing using MATLAB is interfaced with Arduino board that transmits the signal during occurrence of fear causing by any threats.
The quick development of the Internet-of-Medical-Things (IoMT) brings the medical services environment into another reality comprised of tidy medical tools and applications that give numerous advantages, like remote clinical help, convenient organisation of drugs, constant checking, preventive consideration, and wellbeing instruction. Nevertheless, in spite of the important benefits, this fresh reality raises concerns about cybersecurity and solitude since susceptible IoMT gadgets can access and deal with independently collected patient information. Besides, the persistent advancement of cyberattacks, malware, and zero-day weaknesses requires the improvement of the fitting countermeasures. In this article, we illustrate an intrusion detection and prevention system (IDPS) that can ensure the medical care interchanges that depend on the hypertext transfer protocol (HTTP) and the transmission control protocol (TCP). The primary commitment of this study lies in the way that a functioning learning approach is demonstrated and taken on to retrain progressively the regulated classifiers behind the implemented IDPS. The assessment examination exhibits the effectiveness of this work compared with HTTP and TCP cyberattacks, illustrating how the whole exactness is enhanced in the different re-training stages.
Nowadays, stress has become a major cause of many diseases. According to the World Health organization, stress affects about 280 million people globally (i.e.) 3.8% of the population. Early identification of stress is important to prevent its negative impacts on people and is therefore an important step in the service of humanity and health care. These days, smart devices have become an important part of our lives and it achieved a huge amount of usage. This led to the query of whether smartphones and wearable sensors can identify and prevent stress. Although there are numerous works on mental stress detection that exist in controlled laboratory settings, the number of methods for stress detection in everyday life is inadequate. A variety of studies have been conducted to establish an association between stressful events and human responses using diverse psychological, physiological, physical, and behavioral measures. This paper conducts an integrated review of human stress detection and it provides an analysis of strategies and techniques that have been proposed for detecting human stress. This study also focuses on the prospective strategies, advantages, and challenges of mental stress detection using machine learning (ML), and wearable sensors.
The blockchain-based decentralized systems must have attention towards security, transparency, etc. however the first application area of blockchain technology is in financial sector (Bitcoin) where smart contracts are used to automate several processes without any human intervention through just permissions from users. In healthcare industry privacy, security of EHR’S (electronic health records) are being maintained using Blockchain technology and Some of the other industries where blockchain technology is being explored include supply chain management, real estate, voting systems etc. This paper is a thorough get-through of applications and also its common flaws. Diving deep into the solutions which were suggested by a few authors was also discussed.
Wireless Sensor Networks (WSN) encompasses rapid development in research area through large scale applications. The main objective is to compare four different hardware platforms such as Rene, Mica, Spec and Blue in order to find efficient hardware platform that can provide best performance within certain constraints while working on TinyOS as operating system. The four different hardware platforms are compared on the basis of certain constraints such as data communication, rendezvous(mJ/day), alarm checking, sensing, sleep (units in mJ/day), expected lifetime (units in years). According to this investigation, mica outperforms the four different platforms in terms of decoupling between RF and data path speed, bit rate, and timing accuracy. However, future specifications will be found to be more efficient based on CMOS processes and RF radio technology, which results in lower costs, smaller sizes, lower power requirements, and a decrease in complexity.
