School of Science and Technology 科技學院
Computing Programmes 電腦學系
| Programme | Bachelor of Computing with Honours in Internet Technology Bachelor of Science with Honours in Computer Science |
| Supervisor | Dr. Dani Assi |
| Areas | Intelligent Applications |
| Year of Completion | 2025 |
The specific problem to be solved in this project is the difficulty of obtaining mental health support, which includes barriers such as delayed access to professional help, high costs of therapy, stigma, privacy concerns, and a lack of continuous monitoring and personalized support. These challenges can exacerbate mental health conditions and deter individuals from seeking care. The aim of this project is to develop MoodBuddy, an AI-driven mobile application designed to provide accessible, affordable, and stigma-free mental health care.
The objectives of this project include:
Ultimately, MoodBuddy strives to boost users' mental well-being and foster a kinder, more supportive society.
Demonstration Video
Presentation Video
Figure 1: System Block diagram
Supporting Technologies
Front-End
Back-End
Authentication & User Management
Networking Workaround
Architecture Microservices
User Interface design
Database Design
Collections
Login Module
Figure 2: Authentication Sequence Diagram
When user interacts with login interface using their email and password, this module handles the authentication process using Firebase Authentication. After successful authentication, Firebase returns user information from firebase database and user will be redirected to the home screen, allowing them to access the main features of MoodBuddy.
Chat Buddy Module
When the chatbot loads, it sends the input type (text/audio) and user ID to the backend.
The backend retrieves the user’s MBTI from Firebase and recent mood from MongoDB.
These details are sent to the Gemini API to initialize a personalized chat session.
The user can then interact with the chatbot via text or voice.
User input is streamed to Gemini through the backend, which returns supportive, tailored responses in real time—either as text or audio.
Figure 3: ChatBuddy data flow Diagram
Mood Journal Module
The Journal interface allow user to write new journal entry or view their past record.
When the new entry is submitted, the data will be sent to Backend server vis POST
request and store into MongoDB with user ID and timestamp. Then, user will be
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redirect to the journal tab. Once the journal tab is loaded, the existing journal record is
fetched via GET request and displayed to user.
Figure 4: Mood Journal Data Submission and Retrieval Flow
Mood Insight module
User can select a time basis from mood insight dashboard (weekly, monthly or yearly), backend
server will then retrieves journal records from MongoDB with the given range and processes
the data and returns in a visualized format, such as line chart, pie chart. This helps users
understand their emotional trends
Figure 5: Mood Insight Data Retrieval and Visualization Flow
Core Functionality
Figure 6: MoodBuddy Chat
There is our Chatbot, we can receive different real-time responses with different MBTI.
Figure 7: Record Mode
The user can talk to the chatbot in a “phone call”. The real-time voice input has with <500ms low latency (about 2-3 seconds response time).
Figure 8: MBTI Engine Page
The MBTI question logic optimized using Myers-Briggs theory (e.g., energy source question: “Alone vs. Socializing”) and having their own dynamic result pages.
Figure 9: Insight Page
In the Journal will have some manual tags (e.g., “Joyful,” “Neutral”), contextual tags (e.g., “Friends,”“Weather”) enriching the emotional context. Also, doing donut charts for annual mood distribution.
Test Variables
Response Time
Processing Speed
Error Handling
Throughput
Resource Usage
Toolchain
Outcomes
Procedure
Objectives Achieved
Summary of Achievements
Limitations
Technical
Data & Ethical
Clinical
Technology Enhancements
User Experience
Business Models
Jonathan handles all external affairs include business development, patents write up and public relations. He is frequently interviewed by media and is considered a pioneer in 3D printing products.
After graduating from OUHK, Krutz obtained an M.Sc. in Engineering Management from CityU. He is now completing his second master degree, M.Sc. in Biomedical Engineering, at CUHK. Krutz has a wide range of working experience. He has been with Siemens, VTech, and PCCW.
Hugo Leung Wai-yin, who graduated from his four-year programme in 2015, won the Best Paper Award for his ‘intelligent pill-dispenser’ design at the Institute of Electrical and Electronics Engineering’s International Conference on Consumer Electronics – China 2015.
The pill-dispenser alerts patients via sound and LED flashes to pre-set dosage and time intervals. Unlike units currently on the market, Hugo’s design connects to any mobile phone globally. In explaining how it works, he said: ‘There are three layers in the portable pillbox. The lowest level is a controller with various devices which can be connected to mobile phones in remote locations. Patients are alerted by a sound alarm and flashes. Should they fail to follow their prescribed regime, data can be sent via SMS to relatives and friends for follow up.’ The pill-dispenser has four medicine slots, plus a back-up with a LED alert, topped by a 500ml water bottle. It took Hugo three months of research and coding to complete his design, but he feels it was worth all his time and effort.
Hugo’s public examination results were disappointing and he was at a loss about his future before enrolling at the OUHK, which he now realizes was a major turning point in his life. He is grateful for the OUHK’s learning environment, its industry links and the positive guidance and encouragement from his teachers. The University is now exploring the commercial potential of his design with a pharmaceutical company. He hopes that this will benefit the elderly and chronically ill, as well as the society at large.
Soon after completing his studies, Hugo joined an automation technology company as an assistant engineer. He is responsible for the design and development of automation devices. The target is to minimize human labor and increase the quality of products. He is developing products which are used in various sections, including healthcare, manufacturing and consumer electronics.
| Course Code | Title | Credits | |
|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | |
| COMP S333F | Advanced Programming and AI Algorithms | 5 | |
| COMP S351F | Software Project Management | 5 | |
| COMP S362F | Concurrent and Network Programming | 5 | |
| COMP S363F | Distributed Systems and Parallel Computing | 5 | |
| COMP S382F | Data Mining and Analytics | 5 | |
| COMP S390F | Creative Programming for Games | 5 | |
| COMP S492F | Machine Learning | 5 | |
| ELEC S305F | Computer Networking | 5 | |
| ELEC S348F | IOT Security | 5 | |
| ELEC S371F | Digital Forensics | 5 | |
| ELEC S431F | Blockchain Technologies | 5 | |
| ELEC S425F | Computer and Network Security | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| ELEC S201F | Basic Electronics | 5 | |
| IT S290F | Human Computer Interaction & User Experience Design | 5 | |
| STAT S251F | Statistical Data Analysis | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| COMPS333F | Advanced Programming and AI Algorithms | 5 | |
| COMPS362F | Concurrent and Network Programming | 5 | |
| COMPS363F | Distributed Systems and Parallel Computing | 5 | |
| COMPS380F | Web Applications: Design and Development | 5 | |
| COMPS381F | Server-side Technologies and Cloud Computing | 5 | |
| COMPS382F | Data Mining and Analytics | 5 | |
| COMPS390F | Creative Programming for Games | 5 | |
| COMPS413F | Application Design and Development for Mobile Devices | 5 | |
| COMPS492F | Machine Learning | 5 | |
| ELECS305F | Computer Networking | 5 | |
| ELECS363F | Advanced Computer Design | 5 | |
| ELECS425F | Computer and Network Security | 5 |
