scholarly journals Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

2022 ◽  
Author(s):  
N.S.Gowri Ganesh ◽  
Suresh Kumar Pittala ◽  
Ravindrakumar S. ◽  
Senthilkumar V.M.
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Cloud Environment ◽  
Fetal Ecg ◽  
Input Structure ◽  
Fetal Movements ◽  
Electrocardiogram Monitoring ◽  
Effective Diagnosis

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

scholarly journals Prenatal Abdominal Electrocardiogram Monitoring System using IoT in Cloud Environment: A MIMO approach

2022 ◽  
Author(s):  
N.S.Gowri Ganesh ◽  
Suresh Kumar Pittala ◽  
Ravindrakumar S. ◽  
Senthilkumar V.M.
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Cloud Environment ◽  
Fetal Ecg ◽  
Input Structure ◽  
Fetal Movements ◽  
Electrocardiogram Monitoring ◽  
Effective Diagnosis

<p>The application of Internet of Things (IoT) for acquiring, analyzing and transmission of medical data is increasing in recent years. Especially in abdominal ECG processing the need is more. Since the fetal movements are random in the abdomen, a single electrode can’t be able to acquire the fetal ECG. So multi-electrodes are used to record the same. At the same time all electrodes will not provide continuous ECG signal due to the fetal movements. The temperature, pressure and heart rate of the mother also monitored for effective diagnosis. This options makes the design a multi-input structure. In existing methods, Multi-input multi-output options are not available. In addition to that the complexity increases if number of input increases. In conventional methods, the complete machine is available in the patient room. But here in this work the product is divided into three units, bedside unit, doctors unit and main server. The bedside unit is an ECG acquisition device developed using a multi-lead heart rate monitor, sensors and microcontroller. Zigbee is used to transmit the information from the patient bedside to doctors unit which makes it wireless. During the movement of the patient also the data can be viewed. The Multi-output data corresponds to fetal ECG, maternal ECG, heart rate, temperature, pressure. The IoT using raspberry pi module connects the doctors unit with the main server. The machine learning algorithms analyze the ECG data of all electrodes and sensor outputs. The multi-outputs are viewed in a Graphical User Interface (GUI). The integration of the system is conducted to construct a complete IoT-based ECG monitoring system and diagnosis in Cloud environment. </p>

scholarly journals Internet of things–based vital sign monitoring system

2020 ◽  
Vol 10 (6) ◽  
pp. 5891
Author(s):  
Alamsyah Alamsyah ◽  
Mery Subito ◽  
Mohammad Ikhlayel ◽  
Eko Setijadi
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Body Temperature ◽  
Internet Of Things ◽  
Monitoring System ◽  
Vital Sign ◽  
Data Retrieval ◽  
Medical Personnel ◽  
Raspberry Pi ◽  
Medical Diagnoses

Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%.

scholarly journals A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6349
Author(s):  
Jawad Ahmad ◽  
Johan Sidén ◽  
Henrik Andersson
Keyword(s):  
Machine Learning ◽  
Pressure Distribution ◽  
Real Time ◽  
Monitoring System ◽  
Pressure Ulcers ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Support Vector ◽  
Processing Unit ◽  
Posture Recognition

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

scholarly journals IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 816-821
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Monitoring System ◽  
Indian Subcontinent ◽  
Current System ◽  
Water Monitoring ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Raspberry Pi ◽  
Time Data

The main source of water in the Indian Subcontinent is Groundwater. It is also the most rapidly depleting resource due to various reasons such as rampant unchecked irrigation and exploitation of groundwater by industries and other organizations. The current system is limited by short communication range, high power consumption and the system monitors only the water level and the report is available only to the consumer i.e. it is a single-user system. Due to the unavailability of a centralized system to monitor and prevent overuse of water resources, sudden water crises have become a major issue in India. This project aims at implementing an IoT (Internet of things) based water monitoring system that monitors the water level and the quality of groundwater and updates real-time data to the database. This system is designed to monitor the groundwater level of an entire village or a town. It updates the people and the concerned government authorities in case of any decrease in water level and water quality below the threshold value, and also monitors the water consumption during a period and predicts exhaustion time. This system predicts the availability of water in the future based on current demand and usage and the recharge rate using machine learning algorithms. The data collected and the analysis of the data is made available in a Public Cloud. The modules are based on Raspberry Pi Zero, sensor nodes and LoRa (Long Range) Module or Wi-Fi module according to the network requirement for connectivity

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

IOT based Heart Attack Detection & Heart Rate Monitoring System

2020 ◽  
Author(s):  
Suchitra Giri ◽  
Ujjwal Kumar ◽  
Varsha Sharma ◽  
Satish Kumar ◽  
Sikha Kumari ◽  
...  
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Heart Attack ◽  
Attack Detection ◽  
Heart Rate Monitoring

Psychological behavior signal analysis for sports person during their activity session

2021 ◽  
pp. 1-10
Author(s):  
Guochun Liu ◽  
Jian Zheng ◽  
Lin Jiang ◽  
Karthik Chandran ◽  
Beenu Mago
Keyword(s):  
Heart Rate ◽  
Signal Detection ◽  
Signal Analysis ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Psychological Skills ◽  
Social Dimensions ◽  
Useful Knowledge ◽  
Structural Problems ◽  
Psychological Behavior

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

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