Time-Series Analysis of Behavioral Patterns from Wearable Device Data for Depression Detection

Authors- Dr. Pankaj Malik

Abstract-Depression is a prevalent and debilitating mental health disorder that significantly affects an individual’s well-being and daily functioning. Traditional diagnostic methods rely on self-reported symptoms and clinical interviews, which can lead to delayed or inaccurate diagnoses. Wearable devices, such as smartwatches and fitness trackers, offer a novel approach for continuous, real-time monitoring of physiological and behavioral patterns, enabling early detection of depressive symptoms. This study explores the application of time-series analysis and deep learning models to detect depression using data collected from wearable sensors, including step count, heart rate variability (HRV), sleep patterns, and activity levels. We propose a predictive framework leveraging Long Short-Term Memory (LSTM) networks, Transformer models, and statistical time-series methods to analyze sequential behavioral data and classify depression risk. Our experiments, conducted on real-world datasets, demonstrate that deep learning-based models outperform traditional machine learning approaches in detecting depressive states, achieving high accuracy and robustness. The findings suggest that wearable-based mental health monitoring can provide objective, continuous, and non-invasive screening for depression, potentially improving early intervention strategies. Future work will focus on enhancing personalization, integrating federated learning for privacy-preserving predictions, and expanding datasets to diverse populations for greater generalizability.

DOI: /10.61463/ijset.vol.13.issue1.141