Internet of Things Based Patient Monitoring System for Comatose Patient
AgroDefend: Smart Fire Prevention Solutions
Arduino Based Smart Irrigation System
IoT-Enhanced Cardiotocography; Real-Time Integration of Vital Parameters for Comprehensive Fetal Monitoring
Smart Home Security: An IoT-Driven Facial Recognition Door Lock using ESP32-CAM
IoT Based Wearable Muscle Strain Detector
Smart Innovations in IoT Technology
Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Smart Water Management by Smart Sensors and IoT: Enhancing Efficiency and Sustainability with Automatic Pump Control Systems
Implementation of IoT Based Forest Fire Detection and Prevention System
A Comprehensive Review of Internet of Things (IoT) in the Automobile Industry and its Diverse Applications
The Internet of Things (IoT) and machine learning are transforming healthcare by enabling real-time physiological data collection through wearable and implantable medical devices (IWMDs). This innovation allows healthcare providers to analyze routine and clinical settings, identify patterns, and predict health outcomes beyond traditional medical environments. IoT-based comatose patient monitoring systems utilize sensors to track vital parameters such as blood pressure, temperature, heart rate, oxygen levels, and brain activity. The collected data is processed by an embedded microcontroller and transmitted through Wi-Fi to a cloud platform, where healthcare providers receive real-time alerts through web dashboards or mobile applications. By integrating machine learning techniques, the system enhances prediction accuracy and enables timely interventions, replacing passive data collection with proactive decision- making, thereby improving patient care. These technologies mark a significant breakthrough in healthcare, offering continuous patient monitoring and early detection of complications. By facilitating rapid diagnosis and treatment for various medical conditions, they enhance the standard of care for critically ill patients and have the potential to improve public health on a broader scale. As these innovations continue to evolve, they may revolutionize healthcare by increasing both the efficiency and accessibility of high-quality medical care.
This study seeks to develop an innovative and robust solution to minimize the risks of fire outbreaks in agricultural areas. Leveraging the capabilities of the Internet of Things (IoT), this system provides continuous, real-time monitoring of farmland conditions to enable early detection of fire hazards. A network of interconnected sensors is strategically deployed across the farmland to measure environmental parameters such as temperature, and smoke levels. These sensors transmit the collected data to a central control unit, which is processed by an advanced algorithm designed to detect potential fire threats. Upon detection, the system triggers an automatic response using relays that activate fire suppression mechanisms such as sprinkler systems or water pumps using motors, effectively containing and preventing the spread of fire. Additionally, the system includes a display module that provides real-time alerts and updates to farmers and relevant authorities, ensuring they can take immediate action when necessary. This solution not only enhances safety but also reduces crop damage and financial losses by addressing fire threats before they escalate.
This paper describes an Arduino-based irrigation system that uses a relay module, DHT11, soil moisture, and rain sensors to construct an intelligent agricultural irrigation setup. To optimize irrigation, the system gathers and analyses data on temperature, humidity, soil moisture, and rainfall. It guarantees that plants get enough water without over watering or waste. This economical and effective solution promotes sustainable water management, increases agricultural productivity at various scales, and reflects the concepts of precision agriculture.
This paper proposes an IoT-based fetal monitoring system using Arduino Uno, aiming to remotely monitor fetal movement, SpO2, BPM, pressure, and temperature. The system utilizes a combination of sensors, including accelerometers, pulse oximeters, pressure sensors, and temperature sensors, interfaced with Arduino Uno. Data is transmitted wirelessly to a cloud server using I2C protocol for real-time monitoring and analysis. Validation tests demonstrate the system's accuracy in capturing fetal parameters and maternal vitals, enabling timely anomaly detection and intervention. The system offers a cost-effective solution for remote prenatal care, with potential applications in underserved regions. Future work involves integrating machine learning for predictive analytics and refining the user interface for improved usability.
The Smart Door Lock leverages advanced facial recognition technology to enhance access control. This system replaces traditional keys or passwords by recognizing and verifying individuals through facial feature analysis. It utilizes the ESP32- CAM module, an affordable development board equipped with a camera, Wi-Fi, Bluetooth, and low- energy BLE capabilities. Its compact structure makes it well-suited for IoT applications. When an authorized individual approaches, the door unlocks automatically, offering both security and ease of use. The setup also incorporates an Arduino Uno microcontroller, capable of handling multiple inputs and outputs for added flexibility. For improved security, an optional fingerprint sensor can be integrated, enabling dual-layer authentication through biometric verification.
Wearable technology has become a powerful tool for real-time health monitoring, offering innovative solutions for various medical and fitness applications. This paper presents an IoT-based wearable muscle strain detector designed to monitor and analyze muscle strain in real time. The system integrates advanced sensors, including surface electromyography (sEMG) sensors and strain gauges, to detect abnormal muscle activity and strain levels. Information gathered by the sensors is processed using a microcontroller and transmitted wirelessly to cloud platform for visualization and analysis. The proposed device is lightweight, portable, and user-friendly, making it suitable for athletes, rehabilitation patients, and individuals prone to muscle-related injuries. The IoT connectivity enables continuous monitoring and integration with machine learning algorithms for predictive analysis and early intervention. This system aims to reduce the risk of severe muscle injuries, improve sports performance, and improve recovery outcomes in clinical settings. The development and testing of this wearable demonstrate its potential as a reliable tool for personalized health monitoring, providing valuable insights for users and healthcare providers.
