IoT-Enabled Neural Network Analysis for Early Detection and Prediction of Mental Depression

In the era of the Internet of IoT (Internet of Things), addressing mental health concerns is paramount. This research explores an innovative IoT-based approach for early detection and prediction of mental depression. Our research uses IoT devices to convert audio inputs into text, enhancing data accessibility and analysis. We employ advanced tokenization techniques to preprocess the textual data efficiently. The core of our solution integrates a deep learning architecture, featuring three long short-term memory (LSTM) layers with variable node sizes and employing the Swish activation function. Our model demonstrates remarkable performance with a 97.23% accuracy on the test dataset. By amalgamating IoT technology with machine learning, this study contributes to automated mental health analysis, offering the potential for timely intervention and support. The high accuracy underscores the system’s ability to analyze textual expressions, promising a future of improved mental health outcomes through IoT-enabled solutions.