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Working IoT, AI/ML and embedded builds with abstracts, component lists, documentation, and developer support — ready for your final-year submission.
Real-Time ECG Signal Acquisition and Anomaly Detection Using BioAmp EXG and Machine Learning
Cardiovascular diseases remain one of the leading causes of death globally, highlighting the urgent need for accessible, continuous, and intelligent health monitoring systems. This project proposes a real-time, low-cost electrocardiogram (ECG) monitoring and classification system using the BioAmp EXG Pill and machine learning techniques. The system is designed to capture biopotential signals non-invasively from the human body using surface electrodes placed on the chest and limbs. These signals are amplified, filtered, and digitized using a microcontroller (Arduino or ESP32), then transmitted to a host computer for processing. The signal processing pipeline involves noise removal, bandpass filtering, baseline drift correction, and R-peak detection to isolate individual heartbeats. From these beats, essential temporal and morphological features are extracted, including RR intervals, QRS duration, heart rate variability (HRV), and frequency-domain characteristics using Fast Fourier Transform (FFT). These features are used to train and validate supervised machine learning models such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) for classifying normal sinus rhythm and various types of cardiac anomalies (e.g., arrhythmias). The experimental results demonstrate the system's capability to accurately differentiate between healthy and abnormal ECG patterns using publicly available datasets (e.g., MIT-BIH Arrhythmia Dataset) for model training and real-time data from the BioAmp EXG for testing. A simple Python-based GUI is also developed to display real-time ECG waveforms along with the classification results, enhancing the system's usability for patients and healthcare providers. The proposed system provides a compact, affordable, and effective solution for personal health monitoring and early detection of cardiovascular issues. It holds great potential for integration into wearable medical devices, remote patient monitoring, and telehealth platforms, making ECG-based diagnostic tools more accessible to rural and underserved populations.
Smart Knee Brace Enhancing ACL Rehabilitation with Integrated Sensor Tech
Anterior Cruciate Ligament (ACL) injuries are among the most common and serious knee injuries, especially among athletes and physically active individuals. The rehabilitation process after ACL reconstruction is long and requires precise monitoring of knee movements to ensure proper recovery and prevent re-injury. Traditional rehabilitation methods often rely on subjective feedback and periodic clinical assessments, leading to potential inconsistencies and lack of continuous monitoring. This project presents a Smart Knee Brace system that integrates advanced sensor technologies with an ESP32 microcontroller to support real-time monitoring and intelligent feedback during ACL rehabilitation. The system is equipped with flex sensors to measure knee flexion angles, force-sensitive resistors (FSRs) to assess weight distribution and pressure during exercises, and an MPU6050 IMU sensor to track joint motion and orientation in three dimensions. All sensor data is processed and transmitted wirelessly via Bluetooth or Wi-Fi to a custom mobile application, enabling users and physiotherapists to track progress, detect abnormal movement patterns, and receive instant feedback. The system also includes optional haptic feedback (via vibration motor) and visual alerts (OLED display or mobile app) to notify the user in case of incorrect motion or overexertion. The entire device is embedded into a soft, ergonomic knee brace to ensure comfort and usability during physical therapy sessions. By combining wearable technology, wireless communication, and smart sensing, this project offers a cost-effective, portable, and data-driven solution for personalized ACL rehabilitation. It enables continuous assessment, improves patient engagement, and empowers clinicians with actionable insights, ultimately leading to faster recovery and reduced re-injury risks.
vechile fuel theft alert system and leak detection
Fuel theft and leakage in vehicles pose serious economic and safety concerns, especially in commercial and fleet operations. This project presents an IoT-based Vehicle Fuel Theft Alert and Leak Detection System designed to detect unauthorized fuel access and accidental leaks in real time. The system is built using a NodeMCU microcontroller connected to a gas sensor that continuously monitors the fuel tank environment for traces of fuel vapors. In case of abnormal fuel vapor concentration—indicating either a potential leak or unauthorized access—the system triggers a buzzer for immediate local alert and sends a notification to a Flask-based web dashboard, enabling remote monitoring and response. This compact and scalable solution enhances vehicle security, supports timely maintenance, and minimizes environmental hazards due to fuel spillage.
Automated Plant Watering System
Manual plant irrigation often leads to overwatering or underwatering, especially when users are unavailable or unaware of the soil’s actual moisture condition. This project proposes an IoT-based Automated Plant Watering System that intelligently waters plants based on real-time soil moisture levels. The system uses a NodeMCU microcontroller connected to a soil moisture sensor to continuously monitor the soil’s water content. When the moisture level falls below a predefined threshold, the NodeMCU activates a water pump to irrigate the plant until optimal soil conditions are restored. Data from the sensor is transmitted to a Flask-based web dashboard, enabling users to remotely monitor soil moisture levels and control the system if necessary. This solution ensures efficient water usage, reduces manual labor, and supports healthier plant growth, making it ideal for both home gardening and agricultural applications.
water level monitoring system
Efficient water management is essential in both domestic and industrial applications to prevent overflow, water wastage, and dry-run damage to pumps. This project presents an IoT-based Water Level Monitoring System that continuously monitors the water level in storage tanks and automates the control of water pumps accordingly. The system is developed using a NodeMCU microcontroller, which interfaces with a water level sensor to measure the current level in real time. Based on predefined thresholds, the NodeMCU can activate or deactivate a water pump to maintain the desired water level. Data is sent to a Flask-based web interface, where users can remotely monitor tank levels and receive alerts if the tank is empty or full. This system offers a smart, low-cost, and scalable solution for water resource management in homes, agriculture, and commercial infrastructure.
WOMEN Safety with Cam and gps and gsm
Ensuring women's safety in real-time situations such as harassment, assault, or medical emergencies is a growing societal need. Traditional mobile applications or manual alert systems are often ineffective during extreme stress or panic, where the victim may be unable to operate a smartphone. To address this challenge, this paper presents the design and implementation of a smart, wearable, IoT-enabled safety device that autonomously monitors vital signs and detects emergency situations based on sudden physiological changes or manual input. The system is powered by an ESP32 microcontroller integrated with a MAX30102 SpO₂ sensor to continuously monitor the user’s heart rate and blood oxygen levels. A sudden spike in heart rate, potentially caused by fear or physical stress, is identified as an emergency trigger. Alternatively, the user can press a panic button to manually activate the alert system. Upon detecting either condition, the ESP32-CAM module captures a real-time image of the surrounding environment. Simultaneously, the device acquires the user’s precise geographic location using a GPS module (GY-GPS6MV2). The gathered data—including live image, GPS coordinates, and alert message—is transmitted via the SIM800L GSM module using SMS to pre-configured emergency contacts such as family members or local authorities. To further enhance system usability and monitoring, a Flask-based web application is developed to facilitate remote access to sensor data and to integrate Twilio API for automated SMS communication. This system operates independently of internet access, making it ideal for remote or low-connectivity environments. The solution is compact, cost-effective, and designed for real-time, autonomous operation. It serves as a reliable technological intervention for enhancing personal safety and can be expanded for broader public safety applications.