Multi-modal Depression Detection using Deep Learning

Stress is one of the most severe concerns in modern life, as it has become the integral part of each and everyone. Stress is the response given by the human body to the challenges and threats that can affect both the mind and body. Early detection of stress plays the crucial role in promoting the timely intervention. So, this project aims to detect stress with multiple data inputs using the deep learning models. Further the methodology of this continues with the pre-processing, feature extraction, data fusion, designing and training a deep learning architecture, neural network and evaluating its performance in terms of binary output, whether the person is stressed or not. Overall, this research contributes to the advancement of stress detection methodologies, offering a promising approach for early intervention and support in mental health care settings.