DCEM-TCRCN: an innovative approach to depression detection using wearable IoT devices and deep learning.

Purpose: Depression is a psychological disorder that has vital implications for society's health. So, it is important to develop a model that aids in effective and accurate depression diagnosis. This paper proposes a Dynamic Convolutional Encoder Model based on a Temporal Circular Residual Convolutional Network (DCEM-TCRCN), a novel approach for diagnosing depression using wearable Internet-of-Things sensors.

Methods: DCEM integrates Mobile Inverted Bottleneck Convolution (MBConv) blocks with Dynamic Convolution (DConv) to maximize feature extraction and allow the system to react to input changes and effectively extract depression-correlated patterns. The TCRCN model improves the performance using circular dilated convolution to address long-range temporal relations and eliminate boundary effects. Temporal attention mechanisms deal with important patterns in the data, while weight normalization, GELU activation, and dropout assure stability, regularization, and convergence.

Results: The proposed system applies physiological information acquired from wearable sensors, including heart rate variability and electrodermal activity. Preprocessing tasks like one-hot encoding and data normalization normalize inputs to enable successful feature extraction. Dual fully connected layers perform classifications using pooled learned representations to make accurate predictions regarding depression states.

Conclusion: Experimental analysis on the Depression Dataset confirmed the improved performance of the DCEM-TCRCN model with an accuracy of 98.88%, precision of 97.76%, recall of 98.21%, and a Cohen-Kappa score of 97.99%. The findings confirm the efficacy, trustworthiness, and stability of the model, making it usable for real-time psychological health monitoring.