Hybrid AI Models for Improved Mental Health Assessment with Wearable Technology and Mobile Sensing

Abstract

Artificial intelligence (AI)-powered mental health monitoring has new opportunities thanks to the quick developments in wearable technology and mobile sensing. In order to enable real-time, continuous monitoring of mental health indicators, such as stress, anxiety, and depression, this project investigates the integration of AI algorithms with wearable and mobile sensors. Artificial intelligence (AI)-powered models can accurately forecast mental health states by utilising data from activities, skin conductance, and heart rate variability. We assess how well-performing a number of well-known algorithms are at predicting mental health disorders from sensor data, including recurrent neural networks (RNN), convolutional neural networks (CNN), random forests (RF), and support vector machines (SVM). The comparison demonstrates that CNNs perform better than other methods when handling multi-dimensional time-series data from wearable, with up to 92% accuracy rates. Although they require greater processing power, RNNs - especially LSTM networks offer reliable long-term prediction capabilities. SVMs have trouble processing the massive datasets that wearable devices usually produce, despite their effectiveness in managing non-linear connections. Real-time applications can benefit from Random Forests because they strike a balance between computing efficiency and accuracy. By collecting both the spatial and temporal aspects of the data, hybrid models that fuse CNNs and RNNs have the potential to significantly increase prediction accuracy, as this study also demonstrates. To overcome the problem of limited labelled data in mental health monitoring, the integration of Generative Adversarial Networks (GANs) for data augmentation is suggested. Overall, this research highlights how wearable technology and cutting-edge It is possible to develop scalable and personalised solutions for mental health therapy by smoothly integrating computational learning algorithms.