A Wearable Infant HealthCare Monitoring and Alerting System Using IOT

Author(s):  
P. Bhuvaneshwari ◽  
Rajnish Mahaseth
2018 ◽  
Vol 82 ◽  
pp. 375-387 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
R. Varatharajan ◽  
Daphne Lopez ◽  
Priyan Malarvizhi Kumar ◽  
Revathi Sundarasekar ◽  
...  

Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.


Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.


Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 352
Author(s):  
Ruonan Li ◽  
Xuelian Wei ◽  
Jiahui Xu ◽  
Junhuan Chen ◽  
Bin Li ◽  
...  

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.


2021 ◽  
Vol 27 (6) ◽  
pp. 520-532
Author(s):  
Ivett E. Ortega-Mora ◽  
Ulises Caballero-Sánchez ◽  
Talía V. Román-López ◽  
Cintia B. Rosas-Escobar ◽  
Mónica Méndez-Díaz ◽  
...  

AbstractAttention allows us to select relevant information from the background. Although several studies have described that cannabis use induces deleterious effects on attention, it remains unclear if cannabis dependence affects the attention network systems differently.Objectives:To evaluate whether customary consumption of cannabis or cannabis dependence impacts the alerting, orienting, and executive control systems in young adults; to find out whether it is related to tobacco or alcohol dependence and if cannabis use characteristics are associated with the attention network systems.Method:One-hundred and fifty-four healthy adults and 102 cannabis users performed the Attention Network Test (ANT) to evaluate the alerting, orienting, and executive control systems.Results:Cannabis use enhanced the alerting system but decreased the orienting system. Moreover, those effects seem to be associated with cannabis dependence. Out of all the cannabis-using variables, only the age of onset of cannabis use significantly predicted the efficiency of the orienting and executive control systems.Conclusion:Cannabis dependence favors tonic alertness but reduces selective attention ability; earlier use of cannabis worsens the efficiency of selective attention and resolution of conflicts.


2021 ◽  
pp. 2000938
Author(s):  
Tae‐Ho Kim ◽  
Jaydon Vanloo ◽  
Woo Soo Kim

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