scholarly journals Sistem Kendali dan Monitoring Cairan Infus pada Proses Tatalaksana Dehidrasi Berbasis IoT

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Marta Diana ◽  
Kemalasari Kemalasari ◽  
Eru Puspita ◽  
Aji Sasongko Jati

Diarrhea is an endemic disease with Potential Extraordinary Events (PEE), often accompanied by death in Indonesia. Globally, at least diarrheal disease has caused 525,000 deaths in children each year, with the most severe threat being dehydration. It takes a system that can determine the degree of dehydration and manage dehydration quickly and appropriately to reduce the mortality rate. This study created a system to assess the degree of dehydration and perform the dehydration management process automatically. The method of determining the degree of dehydration using heart rate parameters and the process of justification is measured physical condition. In the process of dehydration management, intravenous fluid administration is carried out automatically using servo motors. To improve safety in infusion users, infusion volume, flow obstruction, air bubble, and rising blood detection are also carried out. All results will be processed on the microcontroller and will be sent to the ESP32 via serial communication. The data processing results will be connected using the Internet of Things so medical personnel can monitor via the website. The results showed that the average error of heart rate measurement using the moving average method of 0.41%, and the accuracy value in the infusion control system reached 90%.

Author(s):  
M. A. H. Mohd Adib ◽  
N. H. M. Hasni

Driving with brady-tachy syndrome is one of the main causes of car accidents. In order to prevent drivers from brady-tachy driving, there is a strong demand for driver monitoring systems. Other than problems in driving attitudes and skills, road accidents are also caused by uncontrollable factors such as medical conditions and drowsiness. These factors can be avoided by having early detection. Therefore, the brady-tachy heart automotive so-called BT-Heartomotive device is developed. This BT-Heartomotive device can detect early signs of drowsiness and health problems by measuring the heart rate of the drivers during driving. The device also could use the data to send an alert to the passengers that they’re in precaution. The device shows a good accuracy in the detection of the heart rate level. The device comprised three main components; wristband, monitor and integrated mobile applications. Heart rate measurement can reveal a lot about the physical conditions of an individual. The BT-Heartomotive device is simple, easy to use and automated.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.


2021 ◽  
Vol 1831 (1) ◽  
pp. 012020
Author(s):  
Parth Kansara ◽  
Ritwik Dhar ◽  
Riddhi Shah ◽  
Devansh Mehta ◽  
Purva Raut

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158492-158502 ◽  
Author(s):  
Pengfei Wang ◽  
Fugui Qi ◽  
Miao Liu ◽  
Fulai Liang ◽  
Huijun Xue ◽  
...  

2016 ◽  
Vol 23 (4) ◽  
pp. 579-592 ◽  
Author(s):  
Jaromir Przybyło ◽  
Eliasz Kańtoch ◽  
Mirosław Jabłoński ◽  
Piotr Augustyniak

Abstract Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.


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