Internet of Things-based Medical Applications, Wearable Sensor Systems, and Real-Time Health Monitoring

2019 ◽  
Vol 6 (2) ◽  
pp. 49
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Health Monitoring ◽  
Medical Applications ◽  
Sensor Systems ◽  
Wearable Sensor

scholarly journals IoT on Heart Arrhythmia Real Time Monitoring

2018 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Muhammad Alif Akbar ◽  
Satria Mandala
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Title Page ◽  
Wearable Sensor ◽  
Real Time Monitoring ◽  
Heart Arrhythmia

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Monitoring jantung telah populer sejak 5 tahun terakhir. Hal ini ditandai dengan munculnya berbagai produk monitoring jantung berbasis wearable sensor. Umumnya komunikasi yang digunakan pada sistem tersebut adalah menggunakan radio telemetri dengan biaya opera- sional yang mahal. Beberapa riset mencoba menggunakan konsep internet of things (IoT) untuk mengatasi hal tersebut. Namun demikian, desain komunikasi IoT yang ada belum efisien. Ini disebabkan riset yang ada hanya berfokus pada bagaimana hasil baca sensor dapat dipantau secara realtime. Untuk mengatasi hal tersebut, riset ini mengusulkan sebuah arsitektur IoT berbasis cloud untuk memonitor aritmia, salah satu jenis penyakit jantung yang umum ditemukan. Deteksi aritmia yang diusulkan adalah pengembangan algoritma deteksi aritmia berbasis Tsipuras et al, dengan menggunakan deteksi fitur R. Sistem yang diusulkan pada paper ini telah diuji menggunakan dataset MIT-BIH dan menghasilkan akurasi 93.11% terhadap 3 kelas aritmia, yaitu PAC, PVC dan VT. Menariknya, dengan penerapan IoT, efisiensi algoritma deteksi fitur R meningkat 30% dibanding yang diusulkan oleh Pan dan Tompkins. Terbukti dengan rendahnya waktu rata-rata eksekusi tiap sampel data, yaitu sekitar 0.00749 ms.</span></p></div></div></div>

scholarly journals Integration designs toward new‐generation wearable energy supply‐sensor systems for real‐time health monitoring: A minireview

InfoMat ◽  
2020 ◽  
Vol 2 (6) ◽  
pp. 1109-1130 ◽  
Author(s):  
Yongchao Tang ◽  
Xuejin Li ◽  
Haiming Lv ◽  
Wenlong Wang ◽  
Chunyi Zhi ◽  
...  
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Energy Supply ◽  
Sensor Systems ◽  
New Generation

scholarly journals Low Cost Health Monitoring Sytem Based on Internet Of Things Using Email Notification

2021 ◽  
Vol 3 (2) ◽  
pp. 45-52
Author(s):  
I Dewa Gede Hari Wisana ◽  
Bedjo Utomo ◽  
Farid Amrinsani ◽  
Era Purwanto
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Health Monitoring ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Medical Personnel ◽  
The Internet ◽  
Time Data ◽  
Module Design ◽  
The Internet Of Things

Monitoring activities are needed if there are symptoms of a disease that require quick action so that the patient's condition does not get worse, for that we need a system that can notify doctors so they can take action. The patient monitoring system in hospitals is generally still carried out conventionally, among others, nurses or doctors come to the patient's room to check on the progress of the patient's condition, this will be a problem, if the number of medical personnel and facilities is insufficient to monitor. Patients who need special attention for patient care, such as monitoring the patient's breathing rate. The use of the internet of things (IOT), as a device that can work without the help of people, can perform tasks and provide easier and real time data, so that they can access output directly. The purpose of this research is to design an inexpensive health monitoring tool based on the Internet of Things (Respiration Parameters) using a piezoelectric sensor and an ESP32 Wi-Fi module. From the results of the module design taken from 10 respondents, obtained that the average measurement high accuracy (17.76 + 0.61) and the average level of stability of the design has a magnitude of 0.4 so that it can be concluded that using a piezoelectric sensor in this series can obtain good accuracy. This the design can be used to monitor a person's respiration in real-time

scholarly journals The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring

Aerospace ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 64
Author(s):  
Sarah Malik ◽  
Rakeen Rouf ◽  
Krzysztof Mazur ◽  
Antonios Kontsos
Keyword(s):  
Structural Health Monitoring ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Health Monitoring ◽  
Sensor Data ◽  
Management Approach ◽  
Time Data ◽  
Data Quality Management ◽  
Structural Health

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.

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