Internet of things-assisted intelligent monitoring model to analyze the physical health condition

2021 ◽  
pp. 1-15
Author(s):  
Xiaowei Tang ◽  
Fang Li ◽  
Tamizharasi G. Seetharam ◽  
C. Chandru Vignesh
Keyword(s):  
Internet Of Things ◽  
Physical Health ◽  
Health Monitoring ◽  
Well Being ◽  
Health Condition ◽  
False Alarms ◽  
Health Staff ◽  
Potential Health ◽  
Intelligent Monitoring ◽  
Monitoring Model

BACKGROUND: Physical health monitoring may take several forms, from individual quality changes to complex health checks carried out by health staff. Present health issues are detected with monitoring, and potential health problems are expected. Wearable sensors provide users with ease in everyday tracking, although many issues must be addressed in such sensor systems. The devices take a long time to obtain the requisite detection and diagnostic expertise and produce false alarms. OBJECTIVE: In this paper, the Internet of Things-assisted Health Condition Monitoring system (IoT-HCMS) has been proposed to track and analyze the patient physical health condition. METHOD: The proposed IoT-HCMS utilizes the intelligent monitoring model to follow the patient physical health day by day activities and instantaneously generate the health records. The system will indeed support patients in tracking psychological signs to minimize risks to their well-being. RESULTS: The experimental results show that the IoT-HCMS improves accuracy in patient health monitoring and has less response time.

Fog-Internet of things-assisted multi-sensor intelligent monitoring model to analyse the physical health condition

2021 ◽  
pp. 1-19
Author(s):  
Fen Li ◽  
Achyut Shankar ◽  
B. Santhosh Kumar
Keyword(s):  
Internet Of Things ◽  
Physical Health ◽  
Low Cost ◽  
Health Sector ◽  
Health Condition ◽  
Intelligent Monitoring ◽  
Monitoring Model ◽  
Healthcare Monitoring ◽  
Remote Patient ◽  
Experimental Findings

BACKGROUND: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring. OBJECTIVE: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health condition. METHOD: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used. RESULTS: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.

Internet of things-assisted intelligent monitoring model to analyse the physical health condition

2021 ◽  
pp. 1-14
Author(s):  
Liang Zhuang ◽  
Awais Khan Jumani ◽  
Asma Sbeih
Keyword(s):  
Internet Of Things ◽  
Early Stage ◽  
Healthcare Services ◽  
Health Condition ◽  
Specific Aspect ◽  
Intelligent Monitoring ◽  
Healthcare Facilities ◽  
Monitoring Model ◽  
Smart Healthcare ◽  
Patient Health

BACKGROUND: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients’ medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit. OBJECTIVE: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records. METHOD: The advanced IoT sensors can monitor patient health and insert into the patients’ bodies. Information collected can be analyzed, aggregated, and mined to predict diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient’s activities. RESULTS: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption.

Internet of things-based cloud computing platform for analyzing the physical health condition

2021 ◽  
pp. 1-15
Author(s):  
Mengyao Cui ◽  
Seung-Soo Baek ◽  
Rubén González Crespo ◽  
R. Premalatha
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Health Monitoring ◽  
Tracking System ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Computing Platform ◽  
Concept Model ◽  
Patient Health ◽  
Cloud Servers

BACKGROUND: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient’s healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health. OBJECTIVE: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients. METHOD: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient’s eye movement. The collected data are used in the cloud database to evaluate the patient’s health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers. RESULTS: The experimental results show that patient health monitoring is a reliable way to improve health effectively.

scholarly journals Effects of Wellness Conscious Buildings on the Well-being and Comfort of Workers

2019 ◽  
Vol 111 ◽  
pp. 02047
Author(s):  
Sosui Nakamura ◽  
Shin-ichi Tanabe ◽  
Junta Fujisawa ◽  
Emi Takai ◽  
Sayana Tsushima ◽  
...  
Keyword(s):  
Physical Health ◽  
Indoor Environment ◽  
Environmental Control ◽  
Well Being ◽  
Health Condition ◽  
Experimental Results ◽  
Office Workers ◽  
Health It ◽  
Synergy Effects ◽  
Mental And Physical Health

In recent years, Mental and physical health of office workers is regarded as a problem and the office buildings which improve workers’ wellness. The WELL Building Standard was announced with the aim of improving the health condition of building users in 2014. The purpose of this study is to demonstrate the improvement of the health condition of the office workers who work at the office applying WELL Building Standard. To achieve this purpose, low-score office and high-score office for WELL Building Standard scores were created by changing the indoor environment and furniture in the office, and subject experiments in which we perform the work were conducted in each condition. From the experimental results, we propose environmental control and introduction furniture to verify changes in health condition of office workers, to improve the wellness of building users, and to bring synergy effects to health. It was confirmed that working at plural spaces which workers chose themselves.

Psychological and the physical health impacts of forensic workplace trauma

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dipti Mistry ◽  
Lynsey Gozna ◽  
Tony Cassidy
Keyword(s):  
Physical Health ◽  
Health Effects ◽  
Nursing Staff ◽  
Hospital Setting ◽  
Well Being ◽  
Potential Health ◽  
Content Type ◽  
Staff Members ◽  
Workplace Trauma

Purpose Health-care professionals working in inpatient forensic mental health settings are exposed to a range of traumatic and distressing incidents with impacts discussed variously as “burnout”, “compassion fatigue”, “secondary trauma stress” and “vicarious traumatisation”. This study aims to explore the short- and long-term psychological and physical health effects of trauma exposure in the workplace for frontline staff in a forensic setting. Design/methodology/approach Semi-structured interviews were conducted with 14 nursing staff members working in the male personality disorders care stream in a Medium Secure Hospital. Findings Thematic analysis yielded five themes: categories of trauma; how well-being is impacted; ways of coping and managing; protective factors; and systemic factors, with sub-themes within each of the superordinate themes. Practical implications The findings demonstrate that some staff members were affected both physically and psychologically as a result of trauma-focused work whereas other staff members were unaffected. The psychological and physical health effects were broadly short-term; however, long-term effects on staff member’s social networks and desensitisation to working conditions were observed. A broad range of coping methods were identified that supported staff member’s well-being, which included both individual and organisational factors. Staff member’s health is impacted by exposure to workplace trauma either directly or indirectly through exposure to material, and there is a greater need to support staff members after routine organisational provisions are complete. Staff should receive education and training on the possible health effects associated with exposure to potentially traumatic material and events. Originality/value This research has further contributed to understanding the staff needs of nursing staff members working with the forensic personality disorder patients within a secure hospital setting. This research has identified the following service developments: the need for ongoing support particularly after organisational provisions are complete; further prospects to engage in psychological formulations; greater opportunities for informal supervision forums; staff training to understand the potential health impact associated with trauma-focused work; supervisors being appropriately trained and supported to elicit impacts of trauma-focused work on staff members; and additional opportunities to discuss well-being or monitor well-being.

scholarly journals Physical Health of Food Consumers during the COVID-19 Pandemic

2021 ◽  
Vol 10 (6) ◽  
pp. 218
Author(s):  
José Oliveira ◽  
Tânia Santos ◽  
Marlene Sousa ◽  
João M. Lopes ◽  
Sofia Gomes ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Physical Health ◽  
Eating Behaviors ◽  
Fruits And Vegetables ◽  
Eating Habits ◽  
Negative Impact ◽  
Well Being ◽  
Health Condition ◽  
Margin Of Error ◽  
General Physical

The present research aims to analyze the habits observed in the perception of the general physical health condition of Portuguese food consumers in the COVID-19 pandemic. The investigation is focused on indicators such as weight, physical activity, and consumption habits through the adoption of healthy and not healthy food. Centered on a quantitative approach, the research is based on the application of a questionnaire to a sample of 741 Portuguese consumers, between November 2020 and February 2021, a period during which the most severe measures of social isolation were imposed by the Portuguese government, since the beginning of the pandemic. Moreover, the questionnaire was applied to consumers over 18 years old. According to this population, and considering a 95% confidence level and a margin of error of 4%, the sample has a minimum of 601 responses. Being so, the results of this research are representative for the Portuguese food consumers. The theoretical model was estimated using Partial Least Squares (PLS) in the Smart PLS 3.0 software. The obtained results allowed us to conclude that the Portuguese perception of their weight did not change in the pandemic, despite showing that in general, the pandemic had a negative impact on their physical condition. On the other hand, the results show that the Portuguese associate the practice of physical exercise with physical well-being. Respondents also confirm a positive relationship between “positive eating behaviors (such as consumption of fruits and vegetables, low saturated foods and rich in monounsaturated and polyunsaturated fats” and water consumption) and “the perception of physical health in general”. On the contrary, respondents’ perception of the choice of negative eating behaviors (measured by the consumption of products with a high content of salt and sugar, snacks, and processed frozen and pre-cooked foods) have a negative impact on the “assessment of physical health, in the COVID-19 pandemic”. Hence, it was concluded that the Portuguese consider that an eventual increase in weight does not necessarily correspond to a perception of worse physical health; the practice of physical exercise and good eating habits corresponds to a perception of better physical health; the adoption of bad eating habits corresponds to the perception of bad physical health.

scholarly journals E-Health System Based on Statistical Equation Modeling

2019 ◽  
Vol 8 (12) ◽  
pp. 5179-5185
Keyword(s):  
Health Monitoring ◽  
Statistical Tests ◽  
Research Work ◽  
Health Indicators ◽  
Health Condition ◽  
Equation Modeling ◽  
Root Cause ◽  
Monitoring Model ◽  
Statistical Equation ◽  
Selection Of

In this research work, a design has been proposed for the Health Monitoring system that works with statistical equation models. The key advantage of this method is that it can work with different number of health parameters to through light on the health status of a person. The selection of the variables that will form the health monitoring model was done on the basis of three (Pearson, Kendall,spearman) correlation metrics. The root cause analysis based on OLS regression method confirms the mathematical relationship between the health indicators variables. It was found that visceral fat, as health indicator and as a dependent variable can act as function of seven other variables for knowing the health condition of a person. The validation of the model is done on the basis of multiple statistical tests.

scholarly journals Mobile Health Monitoring System

2019 ◽  
Vol 8 (9S3) ◽  
pp. 919-922
Keyword(s):  
Internet Of Things ◽  
Health Monitoring ◽  
Medical Practitioner ◽  
Health Condition ◽  
Health Conditions ◽  
Health Monitoring System ◽  
Cloud Data ◽  
The World ◽  
Mobile Health Monitoring ◽  
The Internet Of Things

Now-a-days in-health has become the leading cause of death in worldwide. It has become very hard to find the health conditions in the patients by the medical practitioner as it needs an experience and knowledge about the disease they are dealing with. The diseases can be affected in many parts of the patient’s body. They are many kinds of places in the human body where the different kinds of health problems is affected with; some are cancer, fever, skin problems, disabilities, infections, inner body diseases, outer body diseases, etc.,. In the era of rapid revolution of Internet of Things (IoT), the sensors for monitoring the patients is every way of feeling their each module of visibility of the things which the world is affected for it and their working of the medicine is created through this development of the Internet of Things for the healthcare centers in worldwide. This paper will be explaining about the architecture for the tool used to monitor the patient’s critical levels and algorithms used for monitoring the patient’s health and resolving the problems for the problems they are suffering for. I will be also explaining the other parameters used in the monitoring of things in which shows the activities of human health condition. The algorithm is also used to predict the next symptom of the disease and know it soon by the sensor which tells a sure about the symptom which the host is been affected with and will be seen and cured with help of the cloud data in which the whole information Is been stored about the disease and cure for it which is linked to the single cloud which the information is collected and resolving the problems the problems they have been suffering for it

scholarly journals The longitudinal impact of a chronic physical health condition on subjective well-being.

2021 ◽  
Vol 40 (6) ◽  
pp. 357-367
Author(s):  
Caroline Debnar ◽  
Valerie Carrard ◽  
Davide Morselli ◽  
Gisela Michel ◽  
Nicole Bachmann ◽  
...  
Keyword(s):  
Physical Health ◽  
Well Being ◽  
Health Condition ◽  
Longitudinal Impact ◽  
Subjective Well Being

scholarly journals Social Relationships in Older Adulthood and Links With Psychological and Physical Well-Being

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 213-213
Author(s):  
Emily Willroth ◽  
Patrick Hill
Keyword(s):  
Physical Health ◽  
Social Interactions ◽  
Social Relationships ◽  
Daily Diary ◽  
Well Being ◽  
Potential Health ◽  
Older Adulthood ◽  
Diary Data ◽  
Sense Of Purpose ◽  
Physical Health Outcomes

Abstract Positive social relationships are fundamental to psychological and physical well-being across the lifespan. This symposium showcases rigorous daily-diary and longitudinal investigations that (a) examine change in social relationships and loneliness in older adulthood, and (b) investigate links between social relationships and psychological and physical well-being outcomes in older adulthood. First, we present results from a coordinated analysis of three longitudinal studies demonstrating that loneliness tends to increase across the second half of life (Talk 1). Second, we share converging evidence that suggests positive social relationships tend to decline with age. In turn, these longitudinal changes in loneliness and social relationships predict later physical health outcomes (Talk 2). Together, these findings suggest that positive social relationships tend to decrease and loneliness tends to increase with age, resulting in physical health costs. In the second half of the symposium, we turn to research on how positive social relationships may promote psychological well-being, and in turn, better physical health in older adulthood. Using daily diary data, we demonstrate that on days when older adults report more positive social interactions, they also report feeling more sense of purpose (Talk 3). Finally, we show that higher sense of purpose and more positive change in sense of purpose in midlife prospectively predicts better physical health in older adulthood (Talk 4). Together, the research presented in this symposium reveals normative declines in social relationships in late life, while also highlighting the potential health benefits of increasing positive social relationships in older adulthood.

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