IOMT-Based Health Monitoring System using Raspberry Pi Pico

This paper presents the research and development of an Internet of Things- (IoT-) based remote health monitoring system for asthmatic patients. Asthma is an inflammatory disease. Asthma causes the lungs to swell and get narrower, making it difficult to carry air in and out of the lungs. This situation makes breathing very difficult. Remote patient monitoring (RPM) is a method of collecting health-related data from patients who are in a remote location and electronically transmitting it to healthcare providers for evaluation and consultation. The aim of this study is to design a monitoring system that allows doctors to monitor asthmatic patients from a remote area. The proposed system will allow patients to measure oxygen saturation (SpO2), heart rate, body temperature, humidity, volatile gases, room temperature, and electrocardiogram (ECG) using various sensors, which will be displayed in an application. This data is then sent to the doctor to monitor the patient's condition and suggest appropriate actions. Overall, the system consists of an Android application, a website, and various sensors. The Android studio and Java programming language were used to develop the application. For the frontend, the website was built using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and jQuery. The system also uses Django, a Python-based open-source web framework, for the backend. The system developed the various sensors using an ESP8266 microcontroller compatible with the Arduino Integrated Development Environment (IDE). The system uses a MAX30100 pulse oximeter and heart rate sensor, a GY-906 MLX90614 noncontact precision thermometer, a DHT11 humidity and temperature sensor, a MQ-135 gas and air quality sensor, and an AD8232 ECG sensor for collecting various parameters that may trigger asthma attacks. Finally, the system developed the Asthma Tracker app and the Asthma Tracker website for remote health monitoring. The system was initially tested on demo patients and later deployed and tested on seven real human test subjects. Overall, the monitoring system produced satisfactory results. The data acquired by the sensors has a high level of accuracy. The system also maintained user-friendliness and low cost.

In terms of livestock monitoring systems, IoT is used to monitor the health metrics such as heart rate and temperature in real time. However, it is this same data that now provides critical data for early detection of health problem, thus enabling timely interventions and optimal practices in the management. Continuous health parameter monitoring and analysis of these health parameters have been improved considerably which resulted in the mortality rates, animal welfare and operational efficiency improved considerably. Real time data collection and processing is done by the IoT enabled livestock health monitoring system by means of heart rate and temperature sensors together with a Raspberry Pi. With a GSM/GPS module integrated with Raspberry Pi, which was connected to a GPIO, it can send data using an ESP8266 module for tracking location and cellular access. Powered by a reliable input source, the system runs constantly and sends data to the cloud where it is monitored remotely at any time through a web interface. If parameters deviate from normal ranges, real time alerts are generated to notify suitable authorities for timely health intervention and improve their livestock management. Results of simulation show a power consumption of about 5 Watts for reliable operation and constant monitoring. Accurate GPS tracking data represent livestock's spatial distribution that serves as a basis for conducting targeted health checks and ensuring optimal use of resources. The system overall improves livestock management through constant delivery of the real time health insights, enabling timely responses and by allowing stable power operation, thus saving operations costs.

Monitoring bedridden patients during their recovery often requires constant attention to notice any significant changes in their health and provide the necessary action on time. Traditional monitoring relies almost entirely on manual observation and any deficiencies in staff could delay necessary actions and reduce their effectiveness. This paper describes the development of a Real-Time Continuous Health Monitoring System for bedridden patients utilizing IoT and Machine Learning. The monitoring system is designed with multiple biomedical sensors, including heart rate, temperature, oxygen saturation and motion sensors, interfaced with an IoT driven microcontroller allowing real-time collection and transmission of data to a cloud platform. Then, the collected data is analyzed using machine learning algorithms to detect anomalies and predict risk for health deterioration. In the event of an abnormal finding, alerts are automatically forwarded to the clinicians or family members of the patient mobile applications. This intelligent system will eliminate the possibility of human error and allow for 24/7 monitoring of bedridden patients which will allow for early detection of health deterioration and shortening of response times.

Cattle farming is one of the major branches of agriculture. People who grew and monitor cattle often don’t have just a few of them. Instead, they have many numbers of cows, horses, oxen, etc. When the number of cattle increases, it becomes tougher for the one who maintains the cattle to check up on their health individually. To resolve this issue, this study aims the development of an IoT system that is capable of monitoring the temperature and heartbeat rate of the sensor periodically. This is achieved using sensors like temperature sensors, heartbeat rate sensors, etc. It also uses software like ThingSpeak, Rasbian OS, etc. The temperature of the cattle is measured with a temperature sensor and transmitted to the raspberry pi. The heart rate sensor also sends data about the cattle's heartbeat rate to the pi. The GPS sends the cattle's latitude and longitude to the module. The information is then sent to the ThingSpeak software. The data collected by the sensors is displayed by ThingSpeak software. The data includes the temperature of the cow, the heartbeat rate of the cattle, and the latitude and longitude details of the cow.

Infant health monitoring is crucial for early detection of potential health issues. Many existing solutions, while effective, are often costly and designed for clinical settings, making them less accessible for home use. Parents require an affordable, easy-to-use solution to monitor their infant’s vital signs, such as body temperature and pulse, in real time. The rapid development of wireless communication technologies and microcontroller systems like Programmable System-on-Chip (PSoC) provides a new opportunity to create effective, low-power, and cost-efficient health monitoring systems tailored for home use. This study focuses on developing a remote infant health monitoring system that integrates wireless data transmission to enable real-time monitoring by parents. The proposed system consists of three modules: On-Child-Device, Child-Device, and Parent-Device. The On-Child-Device, worn by the infant, measures vital signs using a thermistor and an infrared pulse sensor. The data is processed by a PSoC microcontroller and transmitted wirelessly to the Child-Device, which then forwards the information to the Parent-Device for display. The Parent-Device provides a user interface where parents can monitor real-time data, including alerts for abnormal readings. The system’s hardware is built using PSoC technology, offering flexibility in integrating sensors and communication protocols. Software development was carried out using the PSoC Designer environment, which allows for real-time data acquisition, error checking, and wireless communication management. Initial testing of the system demonstrated reliable performance in measuring and transmitting vital signs. The wireless communication between the modules showed minimal latency, with real-time data accurately displayed on the Parent-Device. The use of PSoC technology enabled efficient power consumption, allowing the system to operate continuously for extended periods without frequent battery replacements. The system's modular design also proved adaptable, allowing for potential integration of addition-al sensors in the future. Feedback from users highlighted the intuitive interface of the Parent-Device, making it accessible for non-technical users. However, challenges related to signal interference in certain environments were noted, suggesting the need for further optimization of the communication protocol. The developed remote infant health monitoring system offers a cost-effective, energy-efficient solution for home use. By utilizing PSoC technology, the system provides reliable real-time monitoring of vital signs, ensuring that parents can quickly respond to any health abnormalities. The wireless transmission and user-friendly interface make the system practical for everyday use, filling a significant gap in current infant health monitoring technologies. Future improvements will focus on enhancing the communication robustness and integrating additional health metrics to provide a more comprehensive monitoring solution. Keywords : Telemetric data, Parental device, Health monitoring, Temperature sensor, Pulse measurement, Programmable System on Chip

Remote health monitoring system has been an interesting topic recently among medical practitioners, engineers as well as IT professionals. However, the application of remote health monitoring system where doctor's can monitor patients' vital signs via web is practically new in Malaysia and other countries. Remote health monitoring system is beneficial to the patients and society where the implementation of such system will save hospital bill, waiting time and reduce traffics in the hospital. The objective of this project is to design and develop body temperature measurement device that can be observe by the doctor in real time as well as history data via internet with an alarm/indication in case of abnormalities. In the proposed health monitoring system, heart rate and body temperature wireless sensors were developed, however this paper only focus on body temperature wireless monitoring system. The temperature sensors will send the readings to a microcontroller using Xbee wireless communication. To send the real-time data to health monitoring database, wireless local area network (WLAN) has been used. Arduino with Ethernet shield based on IEEE 802.11 standard has been used for this purpose. Test results from a group of voluntary shows the real-time temperature reading successfully monitored locally (at home) and remotely (at doctor's computer) and the readings are comparable to commercial thermometer.

In this paper, we introduce a multi-dimensional information sensing monitoring system based on wearable devices. The circuit part of the system is FPC (flexible circuit board) layout, integrating master circuit, temperature, heart rate and blood oxygen sensor, wireless communication circuit, and power supply circuit; clothing fabric using nylon matte TPU composite fabric; designed at the rear waist; the system monitors the temperature, heart rate, blood oxygen level and location data in real time, provide comprehensive health data and provide the data interaction function for users and guardians to view the monitoring data at any time; This innovative design is expected to wear smart clothing in the future The development and health management field brings new possibilities.

Asthma is a chronic respiratory condition requiring continuous monitoring to prevent severe exacerbations. Traditional management lacks real-time data integration and predictive capabilities. This research introduces an innovative predictive analytics-driven system that enhances asthma management for disabled patients by integrating a wearable device, an inhaler (metered-dose inhaler) and a mobile application. The wearable device, equipped with an MQ135 air quality sensor, DHT11 temperature and humidity sensor and a MAX30100 SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and heart rate sensor, continuously monitors environmental factors including air quality, humidity, temperature and physiological parameters including heart rate, SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> levels. The inhaler’s touch sensor tracks its usage status (ON/OFF) to ensure proper adherence. A linear regression-based model analyzes these real-time data streams and achieves an accuracy of 0.92 and a mean squared error (MSE) of 0.08, to predict asthma attacks and sends personalized alerts for proactive inhaler use. By integrating real-time environmental and physiological data, this system offers a personalized and data-driven approach to asthma management, significantly improving patient outcomes.

The aim of this project is to make a pet health monitoring system using Internet of Things (IoT) tech that tracks and looks at important health data in real time for pets’ wellbeing. To get key info such as body temp, heart rate, GPS location, and back leg movement patterns, the system uses modern IoT sensors. These data points allow for ongoing monitoring of pet health since they are processed and shown in real-time. To spot problems from the collected data, the system applies algorithms based on thresholds. If temperature or heart rate stats go outside set healthy levels or if abnormal movements in rear legs are noted by the caregiver, an alert is generated and sent out immediately to them. This feature ensures quick responses which help stop possible health issues from getting worse. Health monitoring is made effective through various sensors like a GPS module, temperature sensor, heart rate monitor, along with MPU6050 that detects odd movements. A microprocessor runs the setup that processes information while ensuring connectivity via GSM module even where Wi-Fi isn’t there. This strong design promises reliable performance across different environments. This strategy enhances how pet owners can give preventive care thanks to IoT technology usage.

Accessing the hospitals in emergency situations, from any rural area for providing appropriate medical assistance is the biggest challenge in saving the life of a patient. Also, the patients who have undergone surgery should be monitored on a 24*7 basis. The above problems can be solved with a help of Health monitoring system using IoT. The proposed system continuously monitors the diagnosis parameters such as heart beat, pressure, activeness and temperature using appropriate sensors like heartbeat sensor, pressure sensor, MEMS sensor and temperature sensor. The sensed medical information is collected and stored in the database of the server which is a Raspberry Pi device. The Raspberry Pi processes these data and displays the values on the web page. It also sends the alert messages to the Doctor, Care taker as well as the patients using the GSM module connected to the Raspberry Pi. This enables the review of reports and real-time video of the patient through patient monitoring system from a remote area thereby reducing the periodical visit.

&lt;span&gt;The health care systems in our contemporary countries are advancing rapidly in terms of maturity and professionalism. In an effort to alleviate the current burden on the public health system and boost the popularity of regular health self-checks, this method has been developed for producing prediagnoses that are easier to use, quicker, and more accurate. To ascertain how well the heart is circulating oxygen throughout the body, a pulse test, a painless examination that measures an individual's degree of oxygen saturation, is used. It can be used to evaluate the state of any patient with a disease, particularly those with pulmonary problems. Diseases in these patients could need ongoing observation and care. Our system comes to the rescue in order to resolve this problem. This portable system is simple to use and may be taken anywhere by the subject. The internet of things (IoT) will update the pertinent parameters. This health monitoring system's controller is made up of an adaptor, a saturation of peripheral oxygen (SPO2 ) sensor (a blood oxygen meter), a temperature sensor, a heart rate sensor, a WiFi module, and a liquid crystal display (LCD).&lt;/span&gt;

This paper describes the use of IoT hardware and protocols to build a smart device which assists factory workers and other employees. This gear is a wearable glove device which can be used in various work spaces where power tools are being constantly used. This proposed system is built around a micro-processor acting as the central server, while many sensors are interfaced with microcontrollers, which act as the link for data transmission and perform various tasks. One of the microcontrollers acts as the master, which controls the other microcontrollers attached to various sensors. The master contains an LCD screen and few buttons, which can control the menu being shown on the screen. This can control the other sensors, and read the data in real time. The gloves contain safety features so that the workers are unable to use any dangerous power tools without wearing proper equipment. The glove will act as a security measure in such a way that each tool will have restricted access, according to the level of expertise of the worker. The glove can also restrict the access to the tools which are being used actively during a particular time-frame. The entire data is actively logged by the central server and various other sensors such as heat sensor, temperature sensor and vibration sensors can be attached and monitored by the master glove. The system also has an extra capability of analyzing tone of the workers so that whenever the user gets hurt and shouts in pain, the analysis function can classify the pain and call for medical help accordingly. A simple sweep based camera module is used alongside the central server to record and live-stream the captured video whenever any power tool is switched On. This system proliferates the value of safety of a worker in a factory floor.

Coal mining is one of the most hazardous occupations, where workers are constantly exposed to life threatening conditions such as toxic gases, high temperatures, structural collapses, and poor visibility. To address these challenges and improve miner safety, this project presents a Smart Helmet for Coal Miners using Internet of Things (IOT) technology. The helmet is equipped with a suite of sensors, including a flame sensor, vibration sensor, DHT11 (temperature and humidity) sensor, MQ-2 gas sensor, LDR (light sensor), SPO2 sensor, and a heart rate sensor. These components work together to continuously monitor environmental and physiological parameters in real-time. An IOT microcontroller is used to collect sensor data and transmit it wirelessly to the Blynk IOT platform, allowing remote monitoring via a smartphone. Critical alerts such as gas leakage, fire detection, abnormal heart rate, or potential structural collapse are instantly relayed to the surface team, enabling rapid response to emergencies. This smart helmet enhances miner safety, reduces risk, and ensures timely intervention, demonstrating the potential of IoT in occupational health and safety in underground mining environments

- A sudden seizure is a rapid and early abnormality in the electrical activity of the brain that disturbs a part or the entire body. Nearly 60 million people worldwide suffer from various epileptic seizures, which can occasionally trigger cognitive disorders that can cause severe physical injury to the patient. In this study, we present the development and evaluation of a wearable device for seizure detection and classification in epilepsy patients. Epileptic seizures pose significant health risks, requiring instant detection to ensure timely medical intervention. By measuring blood pressure, oxygen saturation, and heart rate with biosensors, a thorough picture of the patient's condition during a seizure can be obtained. such as temperature, pulse, and accelerometer sensors, to track the physiological indicators linked to seizures. Using machine learning methods, the wearable Device are able to Data from these sensors are processed using advanced algorithms to reliably identify seizure occurrences. The device has the potential to improve patient safety and quality of life by sending out timely signals, according to preliminary research. This strategy emphasizes how crucial easily accessible seizure monitoring tools are to contemporary healthcare. Key Words: Wearable Seizure Detection, Accelerometer. Biosensor, Temperature Sensor, ESP32 Microcontroller, Epilepsy, Alert System.

&lt;span&gt;&lt;span&gt;Electronic health (E-health) uses information and communication technology including electronics, telecommunications, computers, and informatics to process various types of medical information, to carry out clinical services (diagnosis or therapy). Health is the most important asset in human life, therefore maintaining health is a top priority and serious attention needed. Heart rate and body temperature are vital signs that the hospital routinely checks for clinical signs and are useful for strengthening the diagnosis of a disease. In this research monitoring heart rate and body temperature with the wireless sensor network (WSN) method that uses &lt;span&gt;NodeMCU 1.0 as a controller module and wireless as communication between nodes, the wireless network used in this research Wi-Fi network. As a data taker, a DS18b20 temperature sensor and a heart rate sensor (pulse sensor) are needed, which will be displayed by the ThingSpeak web and smartphones. From the test results, the success rate of the system in detecting heart rates is 97.17%. Whereas in detecting body temperature the success rate of the system is 99.28%. For data transmission, the system can send data smoothly at a maximum distance of 15 meters with a barrier.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;