A health monitoring system for vital signs using IoT

Swaroop, K. Narendra; Chandu, Kavitha; Gorrepotu, Ramesh; Deb, Subimal

doi:10.1016/j.iot.2019.01.004

Cited by 140 publications

(73 citation statements)

References 12 publications

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“…The alerts were based on a previously parameterized limit. Swaroop et al (2019) presented the design of a real-time health monitoring system that stores patients' health parameters. The data can be made available to a medical practitioner as an alert and for monitoring a specific health condition.…”

Section: Related Workmentioning

confidence: 99%

A computational model for adaptive recording of vital signs through context histories

Aranda

Bavaresco

Carvalho

et al. 2021

J Ambient Intell Human Comput

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Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

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Section: Related Workmentioning

confidence: 99%

A computational model for adaptive recording of vital signs through context histories

Aranda

Bavaresco

Carvalho

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…The authors in [7][8][9] proposes a framework to uncover the knowledge in a database and brings into the notice the patterns which help for critical decision making. They provide a predictive system for diseases such as heart disease, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorder using many input attributes related to a specific disease.…”

Section: Literature Surveymentioning

confidence: 99%

Patient Monitoring System with Miniaturization of Sensors

Geetha,

Deepalakshmi,

Pande

et al. 2019

IJEAT

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The quality of human life has improved with the technological advancement and miniaturization of sensors. Healthcare sector has adopted the new technology with the results of related research. Still, healthcare services are not easily affordable in developing countries. This paper aims to reduce the expenses of healthcare system through designing a remote healthcare system comprising of three modules. The first module deals with detection of patient’s vitals using sensors. Second module is to collect and analyze the data and can be sent back to the doctor or guardian through Email and/or SMS alerts in case of any emergencies using Arduino. Third module implemented by K nearest neighbor classification algorithm is used to make runtime decisions if the alert message is not answered by doctors. By implementing these three modules, we achieved a better results in prediction and accuracy than the Attribute based credential and Amrita IoT-based Medical.

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“…In addition to pulse rate and temperature as the vital parameters, blood pressure and SpO2 were integrated into the device [11]. This patient monitoring device was further enhanced by the usage of advanced communication technologies such as RFID (Radio frequency identification), Raspberry Pi, Bluetooth and Wi-Fi [12]. Furthermore, the complete function of this integrated system was applied to monitor patients remotely in the ICU (Intensive care unit) [13].…”

Section: Introductionmentioning

confidence: 99%

Integrated healthcare monitoring device for obese adults using internet of things (IoT)

Gupta¹,

Parikh²,

Swarnalatha³

2020

IJECE

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Obesity is a global epidemic, often considered an impending disaster for the world’s population. Healthcare organizations and professionals repeatedly emphasize the negative impacts on obesity in the development of cardiovascular diseases, hypertension and diabetes. The continuous monitoring of physiological parameters; namely SpO2, blood pressure, body temperature and pulse rate are imperative for obese adult patients. IoT is a dynamic field, used extensively in all fields: agriculture, automobile, manufacturing and retail industry primarily for automated remote real-time monitoring. This paper focuses on the implementation of IoT in the healthcare industry for monitoring and evaluating health conditions of obese adults, along with emphasis on the importance of medical data storage. Furthermore, a device is developed with a novel design and system, which not only allows real-time monitoring but also the storage of medical records for multiple patients simultaneously. The device facilitates measurements of these parameters using an Arduino environment and then transmits the data onto an IoT dashboard using a Wi-fi module for remote monitoring for healthcare professionals. The main aim is to provide a suitable device recommended by doctors for patients suffering from obesity, such that doctors can examine patient’s health trends over a period from the stored data for monitoring any changes that could be a symptom of an underlying unnoticed health condition.

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A health monitoring system for vital signs using IoT

Cited by 140 publications

References 12 publications

A computational model for adaptive recording of vital signs through context histories

A computational model for adaptive recording of vital signs through context histories

Patient Monitoring System with Miniaturization of Sensors

Integrated healthcare monitoring device for obese adults using internet of things (IoT)

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