scholarly journals IOT Based Paralysis Patient Health Care Monitoring System

2021 ◽  
Vol 23 (06) ◽  
pp. 431-437
Author(s):  
Ms.N. ReneeSegridReddiyar ◽  
◽  
S. Remina ◽  
S. Sabrin ◽  
M. Subhashini ◽  
...  
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Nursing Care ◽  
Muscle Function ◽  
The Body ◽  
Body Parts ◽  
Affected Area ◽  
Patient Health ◽  
Health Care Monitoring

We all know that the paralysis condition is a loss of muscle function in the body parts. It can affect any part of your body at any time, then probably you may feel pain in the affected area. Technical and Therapeutic innovations are there to improve the quality of life Our goal is to develop a device that should be easy to use and should be affordable which consists of a basic health care monitoring system with nursing care. We know that these people can’t able to convey their messages or needs. To overcome this, we come up with a system that helps these patients to display messages in a very simple motion. This device can be designed to be mounted on the finger or to be inbuilt in their clothes.

scholarly journals Comparisons of Body Image Perceptions of a Sample of Black and White Women with Rheumatoid Arthritis and Fibromyalgia in the US

2015 ◽  
Vol 9 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Josephine E.A Boyington ◽  
Britta Schoster ◽  
Leigh F Callahan
Keyword(s):  
Quality Of Life ◽  
Rheumatoid Arthritis ◽  
Health Care ◽  
Body Image ◽  
White Women ◽  
The Body ◽  
Activity Limitations ◽  
Body Parts ◽  
Mental Function

Objective : To explore the disease-related, body image (BI) perceptions of women diagnosed with, rheumatoid arthritis (RA) and fibromyalgia (FM). Methods : A purposive sample of twenty-seven females participated in individual semi-structured phone interviews to elicit BI perceptions relative to pain, activity limitations and coping measures. Sessions were digitally recorded, transcribed verbatim, and content analyzed. Results : Body image perceptions relative to 5 major themes emerged in the analysis. They focused on Pain, Disease Impact on Physical and Mental Function, Weight, Diseased-Induced Fears and, Coping measures. Pain was a common experience of all participants. Other troubling factors verbalized by participants included dislike and shame of visibly affected body parts, and disease-induced social, psychological and physical limitations. RA participants thought that manifested joint changes, such as swelling and redness, undergirded their prompt diagnosis and receipt of health care. Contrarily, women with fibromyalgia perceived that the lack of visible, disease-related, physical signs led to a discounting of their disease, which led to delayed health care and subsequent frustrations and anger. All but one participant used prayer and meditation as a coping measure. Conclusion : The body image perceptions evidenced by the majority of participants were generally negative and included specific focus on their disease-affected body parts (e.g. joints), mental function, self-identity, health care experiences, activity limitations and overall quality of life. Given the global effect of RA and FM, assessment and integration of findings about the BI perceptions of individuals with FM and RA may help define suitable interdisciplinary strategies for managing these conditions and improving participants’ quality of life.

PatiCare: Patient Health Care Monitoring System

2021 ◽  
Author(s):  
Nimai Chandra Das ◽  
Anastasiia D. Skakun ◽  
Ziaul Haque Zim ◽  
Mehdi Hasan Rafi ◽  
Ahanaf Thamid Akif
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Patient Health ◽  
Health Care Monitoring

scholarly journals An Experimental Evaluation of Hybrid Learning Methodology based Internet of Things Assisted Health Care Monitoring System

2021 ◽  
Author(s):  
Kamal Upreti ◽  
MAHAVEERAKANNAN R ◽  
Raut Ranjana Dinkar ◽  
Sudhanshu Maurya ◽  
Venkatramanan Reddy ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Learning Strategy ◽  
Smart Device ◽  
Modern World ◽  
Support Vector ◽  
Patient Health ◽  
Health Care Monitoring

Abstract In this modern world, every individual uses intelligent devices to lead a day-to-day activity intelligently. Using the latest technologies such as deep learning, the Internet of Things (IoT) forth provides standard prediction and communication abilities to the existing applications to properly provide rich support to the clients. Many commercial and non-commercial organizations almost adapt these technologies to modify their physical records digitally. This paper designed a novel health care monitoring scheme by adapting these technologies to provide an intelligent monitoring system to analyze patients over random instances with periodic intervals. This paper introduced a new learning-based scheme called Deviated Learning-based Health Analysis (DLHA), in which it combines the conventional algorithms such as Convolutional Neural Network (CNN) and the Support Vector Classification (SVM) logic in a transparent manner. The logical evaluations of the proposed approach called DLHA assessed by extracting the layers from the CNN, appending the classification logic of SVM into the CNN layers, and defining a new algorithm to predict patient health intelligently. The association of sensor-based smart device called Smart Health Indicator (SHI) provides significant support to the proposed approach with the association of intelligent sensors such as Heartbeat Analyzer, Body Temperature Estimation Sensor, Breath Sensor, Global Positioning System (GPS), and the useful Internet of Things enabled controller called ESP8266. Using this SHI kit, the patient details are monitoring instantly and reporting it to the remote server periodically to analyze the health summary without any interventions. The proposed deep learning strategy called DLHA acquires the data from the intelligent health care kit SHI and processes it using classification principles. The records collected from the kit were manipulated according to the process of the trained model generated from the previous testing samples of the patients. The dataset used in this system is generated dynamically from the real-time patient health record and processes the testing report of the patient accordingly. The processed record is appended into the dataset for further reference. The resulting section provides proper proof of the efficiency of the proposed approach in a transparent manner with graphical representations. For all this system is more significant to identify and monitor the health details of the patient in clear manner with proper specifications.

Health Care Monitoring System

2019 ◽  
Vol 7 (9) ◽  
pp. 44-48
Author(s):  
Rahul Pawar ◽  
M.M. Sardeshmukh ◽  
Sagar Shinde
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Health Care Monitoring

Review on Health Care Monitoring System

2019 ◽  
Vol 7 (6) ◽  
pp. 1114-1117
Author(s):  
Rahul Pawar ◽  
M.M. Sardeshmukh ◽  
Sagar Shinde
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Health Care Monitoring

scholarly journals Cloud based smart wireless and health care monitoring system

2021 ◽  
Vol 1059 (1) ◽  
pp. 012035
Author(s):  
S Nithya Priya ◽  
M Ranjith Kumar ◽  
N Nibin Sabari Anand
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Health Care Monitoring

scholarly journals Psychometrics of the Computer-Based Relationships With Health Care Provider Scale in Older Adults

2011 ◽  
Vol 19 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Elizabeth H. Anderson ◽  
Patricia J. Neafsey ◽  
Sheri Peabody
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Health Care Provider ◽  
Care Provider ◽  
Patient Adherence ◽  
Internal Consistency Reliability ◽  
Scientific Data ◽  
Patient Health ◽  
Computer Based

The type and quality of the provider–patient health care relationship impacts patient adherence. The study purpose was to convert the 5-item paper and pencil Relationships With Health Care Provider Scale (RHCPS) to a reliable and valid computer-based scale for use with older adults. Outpatient adults (N = 121) older than 59 years were recruited. The RHCPS underwent several iterations documenting internal consistency reliability, content and factorial validity, and scale usability in a computer tablet format. A total of 5 expert judges rated all 5 items as valid, which resulted in a scale content validity index of 1. Cronbach’s standardized alpha was .81. Principal components analysis extracted 1 factor (eigenvalue > 1; confirmed by scree plot) as anticipated. Computer-based RHCPS has the potential to reveal valuable clinical and scientific data on patient–provider relationships among older adults.

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