This research aims to develop a comprehensive mental health prediction system based on Internet of Things (IoT) and deep learning methods. Specifically, this prediction system is especially sensitive to changing stress levels, to allow prompt intervention and personalized support, in response to the critical demand related to precise, convenient, and stigma-free mental health observation. The proposed system utilizes Interval type-2 Fuzzy Gradient recurrent mixed multimodal Convolutional Neural Network (IFG-CNN) that has three major modules. (i) Stress detector component performs a question–answer based evaluation with the help of Robustly Optimized BERT Approach (RoBERTa). (ii) and A facial emotion recognition module takes visual data and uses Scale-Invariant Feature Transform (SIFT) to extract features and then uses emotion classification using a Rotation-Invariant Surface Attention Radial basis function neural Network (RISAR-Net) trained by the white-faced capuchin optimizer. (iii) The classification module is a mental health status module that classifies them into highly stressed, moderately stressed and normal. The proposed model has a better prediction accuracy with accuracy of 97.85% and F1-score of 97.40, mean absolute error of 0.05 and mean squared error of 0.08. This research indicates that the combination of the IoT applications with the deep learning models to predict mental health is successful and offers a scalable and reliable model that could be used continuously to conduct monitoring and intervene in a timely manner.
Shivarkar et al. (Sat,) studied this question.