Authors :
Shashika Lokuliyana; Anuradha Jayakody; Chiranthi Ranasinghe; Raveen Jayasena; Jeynika Tharmaratnam; Hansani Rajapaksha; Javindu Kumarasiri
Volume/Issue :
Volume 7 - 2022, Issue 11 - November
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3F1RHgM
DOI :
https://doi.org/10.5281/zenodo.7395163
Abstract :
This paper presents an Assistive mobile
application in Sri Lanka to support depression. The
framework uses face recognition technologies and
algorithms to identify depressionprediction via machine
learning for the users. The most effective means of
improving the quality of Depression and mental illnesses
at work is becoming increasingly widespread in the tech
industry. Software developers, according to the
International Journal of Social Sciences, have a far higher
risk of depression, burnout, anxiety, and stress than their
colleagueswho execute mechanical activities. Employees'
mental health, as well as the company's total productivity,
is threatened by declining mental health. Researchers
from Stuttgart's Institute of Software Technologies
discovered that developers who are emotionally
exhausted or depressed generate lower-quality code and
are more concerned about missing deadlines. The
objective of this research is to determine the prevalence of
depression among Sri Lankan software engineers. It is
critical not to deal with depression on one's own. They
require a system of loving individuals, such as family
members, friends, coworkers, and neighbors, who enable
them to be themselves. Building and maintaining a strong
support system of people who can provide
encouragement, help to keep moving and involved in
meaningful activity, and help them challenge their
negative thinking is a critical part of an assistive mobile
app. This app provides features such as patient attention,
patient awareness, treatment for patient depression
levels, monitoring patient progress through time series
analysis, collecting patientinformation via chatbot, and
monitoring the improvement of doctor-patient
relationships.
Keywords :
Machine Learning, Depression, Facial Expression Analysis, Treatment, Chatbot
This paper presents an Assistive mobile
application in Sri Lanka to support depression. The
framework uses face recognition technologies and
algorithms to identify depressionprediction via machine
learning for the users. The most effective means of
improving the quality of Depression and mental illnesses
at work is becoming increasingly widespread in the tech
industry. Software developers, according to the
International Journal of Social Sciences, have a far higher
risk of depression, burnout, anxiety, and stress than their
colleagueswho execute mechanical activities. Employees'
mental health, as well as the company's total productivity,
is threatened by declining mental health. Researchers
from Stuttgart's Institute of Software Technologies
discovered that developers who are emotionally
exhausted or depressed generate lower-quality code and
are more concerned about missing deadlines. The
objective of this research is to determine the prevalence of
depression among Sri Lankan software engineers. It is
critical not to deal with depression on one's own. They
require a system of loving individuals, such as family
members, friends, coworkers, and neighbors, who enable
them to be themselves. Building and maintaining a strong
support system of people who can provide
encouragement, help to keep moving and involved in
meaningful activity, and help them challenge their
negative thinking is a critical part of an assistive mobile
app. This app provides features such as patient attention,
patient awareness, treatment for patient depression
levels, monitoring patient progress through time series
analysis, collecting patientinformation via chatbot, and
monitoring the improvement of doctor-patient
relationships.
Keywords :
Machine Learning, Depression, Facial Expression Analysis, Treatment, Chatbot
