A Machine Learning Enabled Approach for Mental and Physical Health Management Using OpenCV, NLP and IOT

Traditional healthcare systems have historically emphasized physical well-being, sidelining mental health concerns. The proposed machine learning and artificial intelligence system, with its all-encompassing strategy takes into account many physical health indicators, including oxygen saturation, heart rate, and body temperature. It introduces a groundbreaking dimension to mental health assessment through an emotion recognition model, utilizing Convolutional Neural Networks (CNNs) with Keras and TensorFlow. This innovative system interprets diverse mental states like happiness, disgust, anger, fear, happiness, sadness, and surprise, neutral using a trained CNN. The results are visualized through the laptop's built-in camera via OpenCV and deployed seamlessly through a Flask server. The web application, powered by the ESP8266WebServer, exhibits sensor data and network architecture. Evaluation of this integrated health management system is facilitated by a virtual doctor through a medical chatbot, leveraging a manually trained NLP dataset. The incorporation of large language models, Langchain techniques, and Hugging Face embeddings in NLP opens avenues for progressive healthcare solutions. The system's potential is further underscored by the creation of an interactive graphical user interface for the AI-enabled chatbot, employing Streamlit, Python packages, HTML5, and CSS3. This dynamic machine learning-based system not only positions itself as a virtual healthcare assistant but also contributes to the ongoing evolution of advanced technical solutions in healthcare. Its continuous learning mechanisms ensure adaptability and sustained enhancement, marking it as a forefront player in cutting-edge healthcare technologies.