The system consists of a non-invasive sensor that captures relevant physiological signals, which are then processed using an Arduino UNO and a Raspberry Pi 3 B+. The Raspberry Pi serves as the main processing unit, running a trained ML model to predict hemoglobin levels from the collected sensor data. The results are displayed on an LCD screen and transmitted via Bluetooth to an Android mobile app for real-time monitoring.

The machine learning model is trained on a dataset of known hemoglobin levels, using features extracted from optical and physiological data. The proposed system offers a cost-effective, portable, and user-friendly alternative to traditional blood tests, making hemoglobin monitoring more accessible, especially in remote or resource-limited settings.

This approach has the potential to revolutionize point-of-care diagnostics by providing an efficient and non-invasive solution for continuous hemoglobin monitoring, reducing patient discomfort and improving healthcare accessibility.

Objectives

1. To develop a non-invasive hemoglobin monitoring system using machine learning techniques.
2. To measure hemoglobin levels without collecting blood samples.
3. To process sensor data using Arduino UNO and Raspberry Pi 3 B+.
4. To predict hemoglobin levels accurately using a trained ML model.
5. To display hemoglobin values on an LCD screen.
6. To send monitoring data wirelessly to an Android mobile application through Bluetooth.
7. To design a portable, low-cost, and user-friendly healthcare device.
8. To provide continuous and real-time hemoglobin monitoring for patients.

The major building blocks of this project are:

• Power supply.
• RASPBERRY pi3 b+.
• Arduino UNO.
• Noninvasive Hemoglobin Sensor.
• LCD display.
• Bluetooth.
• SD card.

Software’s used:

• Arduino IDE.
• Embedded C language.
• Raspbian OS.
• Python Language.
• Machine Learning.

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The proposed system consists of a glucose sensor, temperature sensor, and MQ-135 acetone sensor to measure blood glucose level, body temperature, and breath acetone concentration respectively. These sensors are interfaced with an Arduino microcontroller, which collects and transfers the sensor data to a Raspberry Pi processor. The Raspberry Pi acts as the main controlling unit and is programmed using embedded Linux. A 4×4 keypad and selection switches are provided for entering dataset values and selecting automatic or manual operation modes. Using the collected sensor data and user input, the Decision Tree J48 algorithm processes the information and predicts the chances of diabetes in a person. The prediction result is displayed on an LCD screen. By integrating machine learning with embedded systems, the project provides an efficient, low-cost, and user-friendly solution for early diabetes prediction and health monitoring.

Objectives:

1. To develop an intelligent diabetes prediction system using Machine Learning techniques.
2. To implement the Decision Tree J48 algorithm for accurate early prediction of diabetes.
3. To measure important health parameters such as blood glucose level, body temperature, and breath acetone concentration using sensors.
4. To interface sensors with Arduino and Raspberry Pi for real-time data acquisition and processing.
5. To provide both automatic and manual modes for user-friendly operation.
6. To allow users to enter dataset values through a 4×4 keypad for prediction analysis.
7. To display the diabetes prediction results clearly on an LCD screen.
8. To design a low-cost, efficient, and portable healthcare monitoring system.
9. To improve early diagnosis and reduce the risk of severe diabetes-related complications.
10. To integrate embedded systems and machine learning for smart healthcare applications.

The major building blocks of the project are:

1. Power supply.
2. Raspberry Pi processor.
3. Glucosensor.
4. Temperature Sensor
5. Arduino UNO.
6. 4X4 Keypad
7. Selection switch
8. LCD display
9. MQ-135.

Software’s used:

1. Raspbian OPERATING SYSTEM.
2. Python Language.
3. Express SCH for Circuit design.
4. Decision Tree algorithm.

Block diagram:

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Objectives:

1. Display Glucose Levels Clearly –Show glucose readings on a TFT screen with color codes:

• Blue for low
• Green for normal
• Red for high

2. Read Glucose Aloud –Use a speaker (DFPlayer Mini) to announce the glucose level.
3. Provide Vibration Alerts –Use a vibration motor for alerts:

• Intermittent vibrations for low glucose
• No vibration for normal
• Long vibration for high glucose

4. Make It Easy to Use –Replace the LCD with a TFT screen for better visibility.
5. Ensure Portability –Use Arduino and compact components for an efficient and portable design.

The major building blocks of this project are:

• Power supply.
• Arduino UNO.
• NIR module.
• TFT screen.
• Vibration
• DF mini player.

Software’s used:

• ARDUINO IDE.
• Embedded c language.

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Objectives

1. To develop a non-invasive Hemoglobin measurement system using Arduino Nano and optical sensors for safe and real-time monitoring.
2. To provide a low-cost and infection-free method for measuring Hemoglobin levels without collecting blood samples.

The major building blocks of this project are:

• Power supply.
• Arduino Nano.
• IR and RED LED sensor.
• LCD display.

Software’s used:

• ARDUINO IDE.
• Embedded C language.

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This project makes a use of AD8232ECG module to monitor the ECG signal of the patient. ESP8266 WI-FI module is used for monitoring the ECG signal into the thingspeak cloud along with date and time.

The AD8232 is a neat little chip used to measure the electrical activity of the heart. This electrical activity can be charted as an ECG or Electrocardiogram. Electrocardiography is used to help diagnose various heart conditions. The processed data is then uploaded to the ThingSpeak cloud platform for storage and analysis. To achieve this task microcontroller loaded program written in embedded C language. This integrated system enables real-time monitoring of cardiac health remotely, providing timely interventions when necessary.

The main objective of this project is:

1. Design a patient health monitoring system using AD8232 ECG sensor.
2. Wireless monitoring using IOT thingspeak technology.

The major building blocks of this project are:

• Regulated Power supply.
• Arduino UNO.
• AD8232 ECG module.
• ESP8266 WI-FI Module.

Software’s used:

• ARDUINO IDE.
• Embedded C language.

Regulated Power Supply:

Block Diagram:

video:

All measured health parameters are processed by the Arduino Nano and displayed on an LCD screen for real-time monitoring. The HC-05 Bluetooth module is used to wirelessly transmit the collected data to an Android mobile application, allowing users to easily view and store their health records. The system provides a simple, low-cost, and portable solution for regular health checkups in hospitals, clinics, gyms, public places, and remote healthcare centers. This project helps in early health monitoring, reduces manual effort, and supports digital healthcare management through wireless communication technology.

Objectives

1. To design and develop a smart Health Kiosk system using Arduino Nano.
2. To measure heart rate and SpO2 levels using the MAX30100 sensor.
3. To monitor blood pressure using a digital BP sensor.
4. To measure body temperature using a temperature sensor.
5. To calculate BMI using height and weight measurements.
6. To measure human height using the ultrasonic SR04 sensor.
7. To measure body weight using a load cell weight sensor.
8. To display all health parameters on an LCD screen in real time.
9. To transmit health data wirelessly to an Android mobile application using HC-05 Bluetooth.
10. To develop a low-cost, portable, and user-friendly healthcare monitoring system for regular health checkups.

Components used:

• Power supply
• Arduino UNO.
• Weight sensor.
• SR04 sensor.
• Digital BP sensor,
• Temperature sensor.
• Max30100 sensor.
• LCD display.
• Hc-05 Bluetooth module.

Software’s used:

• Arduino IDE for compiling and dumping code into Microcontroller.
• Express SCH for Circuit design.

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The EEG sensor captures real-time brainwave activity, which is processed by the Raspberry Pi to identify specific mental states such as focus or relaxation. These signals are then translated into commands that trigger the relay module, turning the bulb ON or OFF. The system employs machine learning techniques or predefined thresholds to differentiate between intentional commands and background noise.

This project demonstrates the potential of BCI technology in smart home automation, assistive devices, and neurotechnology applications, offering hands-free control solutions for users with motor impairments. Future enhancements may include integration with other appliances, improved signal classification, and cloud-based analytics for real-time monitoring.

The major features of this project are:

• Brain-Computer Interface (BCI) Technology: Enables direct communication between the brain and external devices.
• EEG Sensor Integration: Captures real-time brainwave activity (e.g., focus or relaxation states).
• Raspberry Pi 3 B+ Based Processing: Processes EEG signals and identifies mental states.
• Relay Module Control: Uses brainwave commands to turn a bulb ON or OFF.
• Machine Learning / Threshold-Based Signal Classification: Differentiates intentional brain commands from background noise.
• Assistive Application Focus: Offers hands-free control for individuals with motor impairments.
• Potential for Smart Home Automation: Can be extended to control various home appliances.
• Scalable for Future Enhancements: Supports integration with cloud-based analytics, improved classification algorithms, and more devices.

The major building blocks of this project are:

1. Power Supply.
2. Raspberry pi3 B+.
3. EEG amplifier bio amp EXG pill.
4. Relay along with Bulb.
5. Arduino nano.
6. LCD Display.

Software’s used:

• Python programming.
• Raspbian OS.
• Express SCH for Circuit design.
• Machine learning.

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 The main objectives of the project are:

• Monitor Stress Levels: Use the GSR sensor to measure skin conductivity and monitor stress or emotional state.
• Track Heart Rate and SpO2: Use the MAX30100 sensor to measure heart rate and blood oxygen levels.
• Measure Body Temperature: Use the MLX90614 infrared sensor to track body temperature.
• Display Data: Show the collected health data on an LCD screen for immediate feedback.
• Wireless Monitoring: Send the data to a mobile app via Bluetooth for remote monitoring.
• Portable Power: Power the system with a Li-ion battery for mobility and convenience.

The major building blocks of this project are:

• TP4056.
• 3.7V Li-Ion Battery.
• Boost converter.
• Arduino UNO
• MAX30100 HEARTBEAT and SPO2 OXYGEN SENSOR.
• MLX90614 TEMPERATURE SENSOR.
• GSR skin current sensor.
• HC-05 Bluetooth Module.
• LCD display.

Software’s used:

1. Arduino IDE for compiling and dumping code into Microcontroller.
2. Express SCH for Circuit design.

video:

The controlling device of the whole system is a PIC microcontroller. ZIGBEE transmitter and receiver modules, ECG plates are interfaced to the PIC microcontroller.

Microcontroller will continuously monitor the ECG signals from sensor and it will transmit over ZIGBEE transmitter and this dt received by ZigBee receiver and display into the PC using MATLAB GUI.

The main objectives of the project are:

1. Continuous ECG signal monitoring.
2. Wireless data transmission using Zigbee Technology.
3. MATLAB GUI based ECG signal plotting on PC.

The major building blocks of the project are:

• Regulated power supply.
• PIC Microcontroller.
• Reset Button
• Crystal Oscillator
• ECG sensor
• LED indicators.
• Zigbee Transmitter, receiver.
• USB TTL.
• PC with MATLAB.

Software’s used:

• PIC-C compiler for Embedded C programming.
• PIC kit 2 programmer for dumping code into Micro controller.
• Express SCH for Circuit design.
• MATLAB.

Regulated Power Supply:

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The Raspberry Pi Zero 2W runs a lightweight CNN model (SEmbedNet or LMUEBCNet) trained for ectopic beat classification using preprocessed ECG spectrograms generated via Continuous Wavelet Transform (CWT). The system classifies heartbeats into categories defined by the ANSI/AAMI EC57 standard, including normal and various arrhythmic conditions (N/S/V/F/Q). The classification result is displayed on an LCD, while the raw ECG signal and classification status are simultaneously visualized on a web dashboard via IoT (e.g., Flask, MQTT, or HTTP server).

The proposed system eliminates the need for cloud connectivity, offering real-time, on-device inference with low latency (~239 ms) and ultra-low power consumption (~0.4 W), making it ideal for wearable and portable health monitoring applications. This solution can effectively transform basic ECG monitors into intelligent diagnostic tools with arrhythmia classification capabilities.

The Arduino UNO is used for monitoring critical health parameters, including ECG, body temperature, and heart rate using the MAX30100 sensor, which measures heart rate and temperature. The Arduino converts the analog sensor data into digital signals, which are then transmitted to the Raspberry Pi through serial communication for further processing.

Additional components include a fall detection sensor to monitor and detect any falls, triggering an emergency alert if necessary. The LCD display provides users with real-time feedback on their health metrics, while the Buzzer alerts users to any abnormal health conditions or emergencies, ensuring immediate attention. An SD card is integrated into the system to store sensor data securely, allowing for later retrieval and analysis. This IoT Thingspeak cloud-based health system combines continuous monitoring, predictive analytics, and emergency prediction to offer timely interventions and improve overall patient care.

The main objectives of the project are:

1. Develop a compact and low-power arrhythmia detection system using Raspberry Pi Zero 2W and lightweight CNN models (SEmbedNet or LMUEBCNet) for real-time classification of ECG signals.
2. Acquire and process multi-parameter health data (ECG, body temperature, heart rate, and SpO₂) through Arduino Uno and additional sensors, ensuring accurate digitization and reliable transmission to the Raspberry Pi for analysis.
3. Enable on-device machine learning inference without cloud dependency, ensuring low-latency (~239 ms), ultra-low power consumption (~0.4 W), and suitability for wearable/portable health monitoring devices.
4. Integrate IoT and user interface components such as ThingSpeak/cloud dashboards, LCD display, buzzer alerts, and SD card storage to provide real-time visualization, emergency alerts, and secure data logging.
5. Enhance patient safety and care through continuous health monitoring, arrhythmia classification, fall detection, and predictive analytics for timely intervention and emergency response.

The major building blocks of the project are:

1. Power Supply.
2. Raspberry pi zero 2W.
3. Arduino UNO.
4. DS18B20 Temperature sensor.
5. MAX30100(heartbeat&spo2) sensor.
6. AD8232 ECG sensor.
7. Fall detection sensor.
8. LCD display.
9. Buzzer.
10. SD card.

Software’s used:

1. Python programming.
2. Express SCH for Circuit design.
3. Raspbian OS.

video: