scholarly journals SISTEM MONITORING REALTIME DETAK JANTUNG DAN KADAR OKSIGEN DALAM DARAH PADA MANUSIA BERBASIS IoT (INTERNET of THINGS)

Foristek ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Aprilia Aprilia ◽  
Tan Suryani Sollu
Keyword(s):  
Heart Rate ◽  
Internet Of Things ◽  
Medical Personnel ◽  
Health Condition ◽  
Sensor Data ◽  
Rate Data ◽  
Heart Rate Monitoring ◽  
Heart Rate Data ◽  
Oxygen Levels ◽  
Iot Platform

Heart rate and oxygen levels in the blood are very important for medical personnel to know the patient's health condition, with the existing real-time of heart rate monitoring system and oxygen levels in the blood based on IoT, it can facilitate the workload of medical personnel. This tool uses the Max30100 sensor to detect heart rate and oxygen levels in the blood and NodeMCU ESP8266 as controlling and sending sensor data to the IoT platform on android and PC wirelessly. The results of sending heart rate data and oxygen levels in the blood on Blynk and web servers have an error alignment average of 1.7% and 0%. and measurements have an average of 87 bpm and 96% of SpO2 in adolescents 20-24 years with the results of the measurements indicate the patient's condition is normal.

scholarly journals Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device

SLEEP ◽  
2019 ◽  
Vol 42 (12) ◽  
Author(s):  
Olivia Walch ◽  
Yitong Huang ◽  
Daniel Forger ◽  
Cathy Goldstein
Keyword(s):  
Heart Rate ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Neural Nets ◽  
Wearable Device ◽  
Sensor Data ◽  
Rate Data ◽  
Mathematical Methods ◽  
Heart Rate Data ◽  
Using Data

Abstract Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

scholarly journals Telemedicine-equipped android interface-based heart rate monitoring

2021 ◽  
Vol 21 (3) ◽  
pp. 1332
Author(s):  
Hanifah Rahmi Fajrin ◽  
Bayu Satrio Adi ◽  
Heri Purwoko ◽  
Irma Permata Sari
Keyword(s):  
Heart Rate ◽  
Data Retrieval ◽  
Small Error ◽  
Monitoring Device ◽  
Rate Data ◽  
Android Application ◽  
Heart Rate Monitoring ◽  
Normal Range ◽  
Heart Rate Data ◽  
Substantial Error

<p>This research aimed to design a device that can monitor heart rate and help nurses or doctors when they need to monitor and retrieve data of patients. By utilizing Android as a displayer makes it easier for nurses to minimize data retrieval time. The principle of the tool is to record the heart rate data received by the ear clip sensor to be processed by the Atmega328 microcontroller, then displayed on the oled LCD and sent to Android phones via HC-05 Bluetooth for display. If the heart rate data is beyond the normal range, the Android application will post a notification in the form of an SMS to the recipient's cellphone. In testing the tool, it uses a comparison device (pulse oximetry) to determine its accuracy. Based on the testing, the heart rate monitoring device had a small error value of 0.32% and had the most substantial error value of 0.81%. The application of the monitoring system in android data can be sent well at a maximum distance of 13 meters, as well as the implementation of telemedicine in the form of a warning (SMS) can work properly.</p>

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Estimation of Dog Emotions by Machine Learning Using Heart Rate Data of Other Dogs

2021 ◽  
Vol 2021 (0) ◽  
pp. 2P1-M03
Author(s):  
Kotaro SATO ◽  
Kazunori OHNO ◽  
Ryoichiro TAMURA ◽  
Sandeep Kumar NAYAK ◽  
Shotaro KOJIMA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Rate Data ◽  
Heart Rate Data

scholarly journals The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes

2014 ◽  
Vol 8 (1) ◽  
pp. 64-69 ◽  
Author(s):  
Matthew Stenerson ◽  
Fraser Cameron ◽  
Darrell M. Wilson ◽  
Breanne Harris ◽  
Shelby Payne ◽  
...  
Keyword(s):  
Heart Rate ◽  
Type 1 Diabetes ◽  
Rate Data ◽  
Heart Rate Data ◽  
The Impact

scholarly journals Validating accelerometry estimates of energy expenditure across behaviours using heart rate data in a free-living seabird

2017 ◽  
Vol 220 (10) ◽  
pp. 1875-1881 ◽  
Author(s):  
Olivia Hicks ◽  
Sarah Burthe ◽  
Francis Daunt ◽  
Adam Butler ◽  
Charles Bishop ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Rate Data ◽  
Free Living ◽  
Heart Rate Data

Data processing techniques of hydraulic bed sensor and doppler radar for elder care

2017 ◽  
Author(s):  
◽  
Bo-Yu Su
Keyword(s):  
Heart Rate ◽  
Doppler Radar ◽  
Detection Method ◽  
Blood Pressure Measurement ◽  
Fall Detection ◽  
The United States ◽  
Heart Beat ◽  
Body Motion ◽  
Sensor Data ◽  
Heart Rate Monitoring

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Population aging is a common phenomenon in a society. The developed country like the United States, eldercare is becoming an important issue nowadays. There are many aspects we need to address for eldercare, including - circulatory system, alimentary system, nervous system and so on. In this research study, we focus on the heart rate monitoring and estimation using a hydraulic bed sensor. In addition, we also develop the fall detection technique using a Doppler radar. The hydraulic bed sensor for heart rate monitoring is placed under the mattress. The sensor system contains four tubes filled with water and uses the pressure sensor to obtain the Ballistocardiogram (BCG) signal. The BCG signal contains the information of heart beat, respiratory rate and body motion. Two algorithms are developed to process the bed sensor data. One uses the Hilbert transform and the other is based on the energy. By using the algorithms we developed, we can extract the heart beat information to estimate the heart rate. The system has been validated in a well controlled lab environment and a nursing house. In addition to the heart rate, the relative blood pressure measurement by using two features extracted from the bed sensor signal has also been developed and validated with 48 people data. The results show high correlation coefficient with the groundtruth. The Doppler radar for human fall detection is mounted in the ceiling. The radar senses the motion of an object and produces outputs based on the Doppler shift effect. We propose an effective method based on Wavelet Transform (WT) for fall vs. nonfall classification. The proposed fall detection classi er can distinguish between the fall and daily activities. The good performance of the proposed detection method has been validated through the data from the lab and in-home environments, with the falls from stunt actors and senior residents. To further improve the performance, we introduce an additional radar mounted on the wall. Based on the same detection method as when using one radar, we extract and concatenate the features from two radars for classification. The result shows outstanding improvement.

Prediction of Menstrual Cycle Phase by Wearable Heart Rate Sensor

2021 ◽  
pp. 1-15
Author(s):  
Junichiro Hayano ◽  
Emi Yuda
Keyword(s):  
Heart Rate ◽  
Menstrual Cycle ◽  
Technological Development ◽  
Unmet Need ◽  
Cycle Phase ◽  
Rate Data ◽  
Menstrual Cycle Phase ◽  
Heart Rate Data ◽  
Rate Sensor

The prediction of the menstrual cycle phase and fertility window by easily measurable bio-signals is an unmet need and such technological development will greatly contribute to women's QoL. Although many studies have reported differences in autonomic indices of heart rate variability (HRV) between follicular and luteal phases, they have not yet reached the level that can predict the menstrual cycle phases. The recent development of wearable sensors-enabled heart rate monitoring during daily life. The long-term heart rate data obtained by them carry plenty of information, and the information that can be extracted by conventional HRV analysis is only a limited part of it. This chapter introduces comprehensive analyses of long-term heart rate data that may be useful for revealing their associations with the menstrual cycle phase.

Heart rate monitoring system using Internet of Things

2021 ◽  
pp. 211-223
Author(s):  
Rishav Singh ◽  
Tanveer Ahmed ◽  
Ritika Singh ◽  
Shrikant Tiwari
Keyword(s):  
Heart Rate ◽  
Internet Of Things ◽  
Monitoring System ◽  
Heart Rate Monitoring
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