Abstract:
Depression is the most common mental health mood disorder worldwide, which
affects functionality and well-being of individuals having significant effect on their
personal, family and social life. The objective ofthis project is to create a system for
pose estimation using deep learning and to make a system of assessment of risk
factor of depression patients depending on pose estimation. In this study, a small
survey was conducted on those people who either have tried to commit suicide or
have had thoughts on attempting suicide due to depression, as a result, 47.1%
participants were found who either had tried or had thoughts of suicidal attempts.
Three postures were found from the survey which were adapted by the individuals in
those situations, being: hair pulling, looking down and slouching postures. In this
project, the Dataset was collected through Google Images and augmentation
applied to reach the required amount. The project is done using Convolutional Neural
Network (CNN). CNN is a type of deep learning model for processing data and
i
. :
:
i
;
was
'■
, TensorFlow was used on the backend. Different stages involved in this project
comprising of data-augmentation, data pre-processing, training and testing through
CNN resulting in the overall accuracy of
.
!
83%, video framing,
classification/detection of postures done by the subject, and generation of depression
flowchart which indicates the amount oftimes/ no. ofseconds a posture is done.
