Sistem Monitoring Frekuensi Denyut Nadi pada Pelari Menggunakan Metode Photoplethysmographic

<p class="Abstract">Lari merupakan olahraga yang efektif untuk membakar kalori. Namun, olahraga ini mempunyai dampak negatif bagi pelari yang mampu memicu serangan jantung sehingga dibutuhkan alat kesehatan untuk mendeteksi frekuensi denyut nadi saat berlari. Tujuan penelitian ini merancang sistem monitoring frekuensi denyut nadi secara real time pada pelari dengan menggunakan easily plugin pulse sensor berbasis photoplethysmographic. Sensor tersebut terdiri atas transmitter dan receiver infrared yang dipasang pada ujung jari tengah yang mana melalui jaringan kulit mampu mendeteksi volume darah. Fitur buzzer digunakan sebagai alarm jika denyut jantung mencapai 170 Beat Per Minute (BPM). Alat ini juga dilengkapi dengan aplikasi Android yang memudahkan pihak lain memonitoring keadaan denyut nadi pelari. Bluetooth HC-05 sebagai modul komunikasi data antara Arduino dan Android. Alat yang dirancang memiliki error maksimum sebesar 0,73% berdasarkan data percobaan dari 5 partisipan. Berdasarkan hasil pengujian, sistem monitoring frekuensi denyut nadi secara real time mampu mendeteksi serangan jantung saat berlari dan adanya fitur data logger digunakan untuk rekap medis keadaan frekuensi denyut jantung saat berlari tanpa menggunakan aplikasi smartphone Android.</p><p class="Abstract"> </p><p class="Abstract"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Running is the most popular workout around the world, because the most accessible, the cheapest and organized sport. However, running is dangerous in people suffering from heart disease. Hence, the medical device to detect heart failure for runners is required. In this paper, a monitoring system and data logger for detecting heart pulse by using easily plugin pulse sensor heart beat and its implementation for runners is newly proposed. The proposed method of sensor to detect the heart beat by using Photoplesthymograph principle. The sensor consists of transmitter and receiver infrared through to skin tissue to detect the blood volume. Different to previous work, the proposed device can be real time to monitor runners while running and have alarm when their heart beat reach 170 BPM. This device is also equipped with an Android application that facilitate other parties to monitor the runner’s heart beat. By using OMRON HEM-7203 as comparison devices, the rate error of measurement result is 0,086% within 5 participans. The proposed device is suitable for heart pulse monitoring system for runners in real time to reduce the heart attack while running.</em><em> </em></p><p class="Abstract"><em><strong><br /></strong></em></p>

Enhancing Healthcare through Detection and Prevention of COVID-19 Using Internet of Things and Mobile Application

The main symptoms of COVID-19 are high temperature, throat infection, and irregular heartbeat. An integrated wearable device has been presented in this paper for the measurement of temperature and heartbeat in real time using different sensors and NodeMCU ESP8266. For temperature, the DHT11 sensor is used and, for heartbeat, the pulse sensor is used. After reading the data from the sensors processed by NodeMCU ESP8266, it is sent to the firebase database using wireless connection (Wi-Fi module). From the database, the data are displayed in an android application. On the basis of certain conditions of the data, the user as well as the administrator is notified regarding the user’s current health. For the social distancing, an ultrasonic sensor is used. The sensor will warn the user, if he/she is in close contact with someone within a specified distance. The user’s current location is also tracked using the location services of android. A module named COVID-meter, based on the disease.sh-Open Disease Data API, was also included in the research for reading of real-time data of different countries related to COVID-19 like total cases, total deaths, total recovered patients, and so on. The proposed device can be used in both populated and rural areas, but in rural areas it will be much more important because people are unable to reach a doctor on time; thus, they can check their health conditions remotely using the proposed device.

Microcontroller Based Portable Incubator Monitoring Tool with Short Message Service (SMS) notifications

The aim of this research was applied a microcontroller, temperature sensor, weight sensor, heart rate sensor and GSM module to monitoring and notification of the condition of premature babies in portable incubators. The hardware used consists of a DS18B20 sensor, Load Cell, Pulse Heart Rate Sensor, Buzzer, LCD and SIM800L Module. The results showed the Pulse sensor and DS18B20 sensor could measure and detect the baby’s heart rate and baby temperature. The result was on the LCD with an average error of 4.354% for heartrate and 1.437% for temperature. The loadcell sensor can detect weight with an error of 2.16%. The duration of sending SMS to Smartphone is 8s for each delivery. SMS was sent if the baby weak and critical condition.

Guardian device for women - a survey and comparison study

Nowadays women are facing many problems; the most common problems are mental and physical harassment. In order to secure women from such harassments, we propose a novel device Guardian Device for women. The device works with a trigger, microcontroller, GSM module, GPS module, IoT module, Neuro Stimulator, Buzzer, Vibrating Sensor, Pulse sensor, Solar charging battery and a button sized camera. The women can switch ON the trigger whenever they feel danger. The device will activate the global positioning system (GPS), which obviously will track the exact position of the device. The device sends an emergency message to the registered mobile number and nearby police station. The button sized camera captures the image of an attacker and sends the copy to the police stations nearby. Neuro stimulator will give unethical shock to the attacker and a buzzer gives an alarm which alerts the people surrounding the victim.

Universal Nanopore Platform Integrating Multiple Resistive Pulse Sensors into a Single Microfluidic Device

Resistive pulse sensors have been used to characterise everything from whole cells to small molecules. Their integration into microfluidic devices have simplified sample handling whilst increasing throughput. Typically, these devices measure a limited size range or a specific analyte, making them prone to blockages in complex sample matrixes. To prolong their life and facilitate their use, samples are often filtered or prepared to match the sample with the sensor diameter. Here, we advance our tuneable flow resistive pulse sensor which utilises additively manufactured parts. The sensor allows parts to be easily changed, washed and cleaned, its simplicity and versatility allows components from existing nanopore fabrication techniques such as silicon nitride, polyurethane and glass pipettes to be integrated into a single device. This creates a multi-nanopore sensor that can simultaneously measure particles from 0.1 to 30 m in diameter. The orientation and controlled fluid flow in the device allows the sensors to be placed in series, whereby smaller particles can be measured in the presence of larger ones without the risk of being blocked. We demonstrate the device with a range of nanopore materials commonly found within the literature, the easiest to set up was the pulled glass pipette and glass nanopore membrane. However, the glass nanopore membrane was by far the most robust and reusable component tested. We illustrate the concept of a multi-pore flow resistive pulse sensor, by combining an additively manufactured tuneable sensor, termed sensor 1, with a fixed nanopore sensor, termed sensor 2. Sensor 1 measures particles 2 to 30 m in diameter, whilst sensor 2 can be used to characterise particles as small as 100 nm, depending upon its dimensions.

Development of a Sensor-Based Heartbeat and Body Temperature Monitoring System for Remote Chronic Patients

The growing number of chronic diseases have stretched the healthcare sector. Globally, more than 36 million deaths per year are attributed to chronic disease complications. This has increased the demand for telemedicine in managing chronic patients as they must be on continuous monitoring for a long time. The involvement of wireless sensor networks and cloud computing technology in the health sector is increasing due to the potential it possesses in remote sensing and monitoring applications. This paper presents a developed system prototype for monitoring the heartbeat rate and body temperature of chronic patients using sensors. The monitored data are sent to a cloud database in real-time via an internet connection using the ESP8266 wireless module. The approach involves connecting a heart pulse sensor, an MLX90614 contactless temperature sensor, and the ESP8266 module to the Arduino development board. The goal of this work is to create a system that interfaces chronic patients and medical personnel in an attempt to avert the effects of insufficient health facilities, especially in rural Africa. The patient’s data in the cloud database can also be retrieved by medical personnel anytime in order to track the patient’s conditions and to advise the patient accordingly. The sensed heartbeat and body temperature readings were processed, sent, and recorded in the cloud database effectively.

Klasifikasi Data Aktivitas Setelah Joging Menggunakan Fuzzy Logic

One of the routine activities that cause a lot of body fluids is jogging. Research shows that excessive jogging can disrupt the balance of body fluids so that you tire quickly in the long run. As a result, the body releases too much fluid. This makes someone forget or underestimate the need for fluids in the body. In this study, a detection system for body temperature, ambient temperature and heart rate was built for the classification of dehydration in the body to maintain fluid stability in the body. The system is built using the Pulse Sensor, Mlx90614, OpenWeatherAPI and the Android Platform. This study uses the Mamdani Fuzzy Logic method to determine the classification of user dehydration. The results of the research analysis contained a calibration test of the MLX90614 sensor against the Thermogun with an Error Rate value of 2.01% and an RMSE value of 0.9. Testing the Pulse Sensor against the Oximeter produces an Error Rate value of 1.54% and an RMSE value of 0.7. There is a difference in the difference in Deffuzification values ​​due to differences in the fixed points for each library. Matlab fixed point with a value behind the three digit point, 16 digit Fuzzy Sci-kit and the Builded System using a 15 digit point value.

Predictive Analysis for Real Time Stress Detection

Stress can be characterized as a feeling of either emotional or physical tension. Due to the biology of the human body, it releases some hormones when under stress. These hormones might cause tensed muscles, increase pulse rate or the heart rate, increase brain activity to make the brain more alert to the surrounding. Stress can be predicted well before it happens by constantly measuring the Heart Rate Variability (HRV) parameters obtained using the pulse sensor. In this project a supervised machine learning model is created using the data acquired from Physionet, once the data is acquired it is cleaned and the missing data is filled. This data set is later used to create a random forest classifier and is saved using pickle library. Once the model is created it is used to detect stress in real time. Pulse sensor amped is used to get the required pulse data in the form of a CSV file and a numpy array is created using inter beat interval information got from pulse sensor. Once a numpy array is created neurokit2 library is used to extract the HRV information of the R-R interval. Later these parameters are compared with the created model and checked to see if the subject is stressed, if the model detects the subject as stressed an alerting message is sent to the subject’s smartphone using Twilio.