An Enhanced IoT-Based Array of Sensors for Monitoring Patients’ Health

Odusami, Modupe; Misra, Sanjay; Abayomi‐Alli, Olusola; Shobayo, Olamilekan; Moses, Chukwuebuka

doi:10.1007/978-3-030-81473-1_5

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…In the year 2022, a novel patient health monitoring device was created utilizing the Internet of Things and four distinct sensors that quantified temperature, heartbeat, pulse, and UV radiation. The study conducted by Odusami et al (2022) revealed that the ESP8266-01 Wi-Fi module data was stored in the cloud via Thing Speak. This approach led to an enhancement of the measured parameters, deployment environment, and response time.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Monitoring System for the Elderly based on the GSM Network

Hassan

2023

JLMA

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Infectious illnesses are more prevalent and harmful in older adults because to functional, demographic, and immunological changes brought on by aging. Treatment for these disorders is complicated by aging organ systems. Given that many seniors have multiple illnesses and take multiple medications, antimicrobial therapy for them should be carefully chosen to prevent drug interactions and other issues. Thus, it is important to keep an eye on the health of elderly people. In this research, we tracked elderly patients using a clever technique. The device includes a microprocessor, temperature sensor, pulse sensor, LCD display, and GSM module. The temperature and pulse of the elderly are shown on an LCD monitor. The system may notify emergency services if the pulse and temperature data go over the permitted limitations. The method was successfully tested on a large number of elderly persons.

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Section: Related Workmentioning

confidence: 99%

A Real-Time Monitoring System for the Elderly based on the GSM Network

Hassan

2023

JLMA

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“…However, recent developments in easy-to-use wearable devices have facilitated continuous long-term monitoring of physiological signals such as ECG or PPG signals [ 25 ]. These technologies augmented with interpretable and accurate artificial-intelligence-based models offer the prospect of not only reducing the cost of monitoring, but also improving patient management through data interpretation and clinical decision support, particularly in low-resource settings [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries

Ghiasi

Zhu

Lü

et al. 2022

Sensors

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Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interepretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.

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“…These drugs can cause side effects such as dry tongue, constipation, loss of appetite, excessive hunger, mild dizziness, stomach irritation, delayed menstruation, excessive/deficient menstrual bleeding [21][22][23]. Also, in the early stages of a serious mental illness, counseling may not be helpful.…”

Section: Introductionmentioning

confidence: 99%

IoT-TPMS

Logeshwaran¹,

Malik

Adhikari

et al. 2022

ijhs

160

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In general, the number of diseases is increasing in the current era. There is also a growing fear among the patients about the nature of the growing number of new diseases and their consequences. Thus the patients are interested in getting treatment from healthcares for minor physical problems. But factors such as lack of space in healthcares and lack of time for doctors make patients uncomfortable. Sometimes doctors recommend that patients come to the healthcare only for emergency treatment. In this paper, a triangular method is proposed which takes into account the needs of the patients, calculates the time management of the doctors and analyzes the facilities available in the healthcares. Designed with the help of medical IoT devices, efficient sensors fitted to patients' bodies monitor their physical condition. Furthermore based on this sensor information the doctor can provide the patient with the necessary instructions from where they were. These sensors send information directly to the healthcare when further emergency treatment is needed. Thus healthcares can make the necessary arrangements to provide the necessary treatments to the patient immediately.

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An Enhanced IoT-Based Array of Sensors for Monitoring Patients’ Health

Cited by 3 publications

References 38 publications

A Real-Time Monitoring System for the Elderly based on the GSM Network

A Real-Time Monitoring System for the Elderly based on the GSM Network

Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries

IoT-TPMS

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