Published Paper Details:

In recent years, prolonged screen usage and sedentary lifestyles have significantly increased the prevalence of poor posture, leading to various health issues such as back pain, neck strain, and reduced productivity. This project presents a comprehensive smart posture monitoring system that integrates Internet of Things (IoT) technology with real-time computer vision and machine learning techniques to address this problem effectively.The system utilizes a personal computer equipped with a webcam to continuously monitor the user's posture. Using advanced computer vision frameworks such as OpenCV and MediaPipe, key human body landmarks are extracted and analyzed in real time. These extracted features are then processed using a machine learning model based on the K-Nearest Neighbors (KNN) algorithm, which classifies posture into categories such as "Good" and "Bad." The trained model ensures accurate and efficient posture detection under various conditions.In addition to posture analysis, the system incorporates physiological monitoring through a MAX30100 pulse oximeter sensor connected to an Arduino Nano. This sensor measures vital parameters such as heart rate and blood oxygen saturation (SpO?), providing additional insights into the user's health condition. The collected sensor data is transmitted to an ESP32 microcontroller, which acts as a communication bridge and sends the data to the cloud using the Blynk platform. The ESP32 also displays real-time data on an I2C LCD screen for local monitoring.When improper posture is detected, the system immediately triggers multiple alert mechanisms to ensure user awareness and corrective action. These include an audio alert generated using text-to-speech, an email notification with an annotated image of the detected posture, and a signal sent to the microcontroller for visual indication. The integration of these features ensures a robust and responsive feedback system.Overall, the proposed system provides a low-cost, non-invasive, and intelligent solution for continuous posture monitoring. By combining IoT, machine learning, and computer vision, it enhances user awareness and promotes healthier sitting habits. The system is highly scalable and can be further extended for applications in workplaces, educational institutions, healthcare monitoring, and smart environments.