Design and build a software or product for Microsoft imagine cup 2024 (A)

Adolescence is a period acknowledged by physiological, emotional, and social changes, with mental disorders such as depression affecting one in seven individuals aged 10 to 19 globally. Neglecting adolescent mental health can have extensive consequences into adulthood, impacting both physical and me...

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Bibliographic Details
Main Author: Ding, Man
Other Authors: Wesley Tan Chee Wah
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176947
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Institution: Nanyang Technological University
Language: English
Description
Summary:Adolescence is a period acknowledged by physiological, emotional, and social changes, with mental disorders such as depression affecting one in seven individuals aged 10 to 19 globally. Neglecting adolescent mental health can have extensive consequences into adulthood, impacting both physical and mental well-being. Some challenges of current mental health diagnosis and treatments involve the high cost of conventional consulting, the subjectivity of traditional mental health surveys, limited awareness in parents and schools, and a lack of early detection mechanisms for depression in teenagers. The primary objective of this project is to develop 'Mind Garden,' an affordable, objective, and proactive depression-detection software for individuals in their adolescence. Besides, building upon existing research on the positive relationship between school connectedness and mental health outcomes, this software is strategically designed to be deployed within educational environments. ‘Mind Garden’ leverages game development and Artificial Intelligence (AI) integration to analyze users' facial expressions, eye movements, and speech components during gameplay, generating a report suggesting the participant’s mental health status accordingly. In this paper, the development of the engaging 2-dimensional game, designed using Unity, and the construction of machine learning models for speech and voice analysis will be discussed in detail