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E-Skin for Futuristic Nanosensor Technology for the Healthcare System
Published in Suresh Kaushik, Vijay Soni, Efstathia Skotti, Nanosensors for Futuristic Smart and Intelligent Healthcare Systems, 2022
Venkateswaran Vivekananthan, Gaurav Khandelwal, Nagamalleswara Rao Alluri, Sang-Jae Kim
collected data helps the medical professionals to accurately diagnose the health issues of the patients. Wearables can also be used on patients when they return home after surgery or an operation to monitor their recovery and ensure no complications occur. This helps ease the burden on healthcare systems by letting the patients leave the hospital and return home, but still keeping an eye on their conditions using wearable devices. Emergencies can also be recognized as soon as they occur. This system can be set up to notify others, such as family members or healthcare professionals. This more proactive approach to healthcare can be very beneficial, as it can catch problems early before they develop into larger issues that could have dangerous health consequences (Trung and Lee 2016, Miyamoto et al. 2017).
Limos—Live Patient Monitoring System
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
T. Ananth Kumar, S. Arunmozhi Selvi, R.S. Rajesh, P. Sivananaintha Perumal, J. Stalin
The patient monitoring systems started blooming in the mid-1960s. The Technicon Medical Information System was the first and most successful system, begun in 1965 as a collaborative project between Lockheed and El Camino Hospital in Mountain View, California (Pramila et al., 2014). Patient monitoring is the examination of the medical condition of the critical organs and other vital parameters of a patient over a particular period of time. It has the facility to evaluate the condition of a patient’s health by checking without letting any disease or ailment to further impediments the body. The vital health parameter includes heart rate, respiration rate, body temperature, glucose level, stress, hypertension, and blood pressure. These parameters are continuously observed by using the respective sensors and the monitored information will be transmitted to the medical practitioner using different technologies like Wi-Fi, GSM, and wireless sensor network. If any deformities or any variation from the normal value of such parameters are detected, then an alert or warning signal will be sent to the medical practitioner and nurse. So, the medical practitioner can be able to provide treatment for the health disorder in the earlier stage. Thus, the healthcare system continuously monitors the patient(s) information especially just in case of any potential irregularities, in the emergency phase, the alert system connected to the system gives an audio and video cautionary signal that the patient needs immediate attention. From (Pramila et al. 2014; Agarwal, 2013; Adivarekar et al., 2013) to learn the growth the different stages involved in patient monitoring are represented in Table 14.1.
Integrating Cybernetics into Healthcare Systems
Published in Suhel Ahmad Khan, Rajeev Kumar, Omprakash Kaiwartya, Mohammad Faisal, Raees Ahmad Khan, Computational Intelligent Security in Wireless Communications, 2022
Saquib Ali, Jalaluddin Khan, Jian Ping Li, Masood Ahmad, Kanika Sharma, Amal Krishna Sarkar, Alka Agrawal, Ranjit Rajak
Confidentiality of patient health data: Patient health data is typically subject to legal and ethical confidentiality responsibilities. This medical information must be kept private and only approved physicians and nurses should have access to it. As a result, it is critical to keep individual medical information private so that an enemy cannot snoop on patient data. Data eavesdropping can be harmful to patients since the adversary might use the data for any illegal reason, infringing on the patient's privacy. As a result, data confidentiality is a critical necessity in the cybernetics-based healthcare system.
A secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing
Published in Automatika, 2023
D. Pavithra, R. Nidhya, S. Shanthi, P. Priya
Remote patient monitoring uses various types of sensors such as wearable sensors, and biosensors for collecting the health-related data about the patient and this data is first transferred to the hospital server and then data is transferred to the cloud storage from which the data could be accessed by the doctors and the medical team particularly the authorized members for providing their recommendations and assessments about the status of the patient. There is a need to analyse the data collected from the patient and to identify and detect the symptoms much earlier to avoid further complications. With the usage of wearable devices, the heart rate, temperature, blood pressure, blood glucose levels, and other variables are monitored. The hospital's end terminal, the hospital's data processing system, the data gathering system, and the communication network are all parts of the RMS. Data gathering through the use of various sensors or equipment with embedded sensors is known as data acquisition [5]. Data Processing is capable of processing the data with data receiving and transmission capability. Communication network is capable of connecting the data acquisition to the data processing system.
Health care Monitoring System and Analytics Based on Internet of Things Framework
Published in IETE Journal of Research, 2019
The system architecture of health care monitoring systems has to meet the requirements of measuring vital signs of patients, processing and converting sensor signals to readable output and displaying the monitoring results on web applications. In general, the system architecture of the proposed health care system is divided into front-end (presentation layer) and back-end (data access layer). In the front-end layer, the system integrates microcontroller-enabled sensors to measure vital signs of a user and utilises Arduino Nano and Intel Edison to convert the detected signals as readable outputs. Besides sensors, the front-end layer includes a graphical user interface (GUI) or web application which allows both patients and doctors to access and view the obtained data and information. In the back-end layer, the output from the data acquisition system is stored on the cloud database and analysed prior to data visualisation.
Experimental investigation of thin-film solar cells as a wearable power source
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Naresh Bangari, Vinod Kumar Singh, Virendra Kumar Sharma
Advances in design and fabrication of semiconductor technology help miniaturization of sensors, data records, and processors required to processes the data and data transmitters all are tiny in size with less power consumption. Especially wearable sensors or monitors like electrocardiogram (ECG), electromyography, heart pulse, temperature, and stress level sensors will provide high efficiency and better quality of health care to a patient. Obviously, these sensors required the power and this power can be harvested from the environment by using sources like light, thermal, electromagnetic, and kinetic energy.