Deep Learning for Identification of Behavioral Changes

Abstract

This study explores the application of long short-term memory (LSTM) networks for the identification of behavioral changes. LSTM networks, a type of recurrent neural network (RNN), excel at modeling sequential data and capturing long-range dependencies, making them well-suited for analyzing temporal patterns in human behavior. The research investigates how LSTM networks can effectively learn from sequential behavioral data, such as activity logs, physiological signals, or speech patterns, to detect deviations from normal behavioral patterns. By leveraging LSTM's ability to retain information over extended time intervals, the study aims to develop models capable of recognizing subtle shifts in behavior that may indicate changes in mental health, emotional states, or lifestyle habits. Additionally, the research explores techniques to enhance the interpretability of LSTM-based behavioral change detection models, addressing challenges related to model transparency and explainability.