Time-Series Analysis for Mental Health Interventions using LSTM Support for Wearable Biofeedback Devices in the Cloud

This research introduces a new technique to use time-series analytic methods for mental health interventions using cloud-based Long Short-Term Memory (LSTM) networks and wearable biofeedback devices. Innovative approaches to intervention and support are urgently needed since mental health issues are a major health concern worldwide. Physiological data is provided in real-time via wearable biofeedback devices, allowing for individualized therapies. On the other hand, real-time data analysis is not without its difficulties. It presents a cloud-based design that uses LSTM models to examine biofeedback data for trends across time. For precise mental health status prediction and categorization, LSTM models successfully capture time-series data's long-range relationships. The scale and accessibility made possible by cloud computing make it an attractive alternative for broad use. It shows that the suggested method is useful for enhancing mental health treatments by evaluating it using real-world biofeedback data. Our research indicates that cloud-based LSTM-supported time-series analysis might be a game-changer when it comes to improving the reach and effectiveness of wearable biofeedback devices for mental health therapies.