Research Team

The research team consists of undergraduate students specializing in TinyML, Edge AI, and assistive technology development.

Student Researchers

Four undergraduate researchers from SLIIT's Department of Computer Systems and Engineering, each specializing in a critical component of the integrated assistive technology system.

Weragala R.T.L, Face Recognition Specialist

Weragala R.T.L

IT21820946

Face Recognition Specialist

Component 01: Real-Time Facial Recognition

Face recognition with personalized voice feedback, dynamic enrollment, edge AI optimization

Dilini K.D., Sinhala OCR Developer

Dilini K.D.

IT21826740

Sinhala OCR Developer

Component 03: Sinhala Text Recognition (OCR)

Optical character recognition, document identification, voice feedback for reading

A.S.G. Punchihewa, Navigation System Developer

A.S.G. Punchihewa

IT21821486

Navigation System Developer

Component 02: Sinhala Speech Navigation

Speech-to-text, text-to-speech, ultrasonic obstacle detection for safe navigation

Vithanage H.P., System Integration Lead

Vithanage H.P.

IT21159190

System Integration Lead

Component 04: Scene Recognition & Object Detection

Mobile app development, hardware integration, system deployment, scene understanding

Academic Supervision

D

Dr. Dharshana Kasthurirathna

Supervisor

Faculty of Computing | Computer Science

Senior Lecturer specializing in AI, machine learning, and edge computing applications for assistive technology.

M

Ms. Hansi De Silva

Co-Supervisor

Faculty of Computing | Software Engineering

Lecturer in Software Engineering with expertise in human-computer interaction, accessibility, and mobile application development.

Institution Details

Institution

Sri Lanka Institute of Information Technology (SLIIT)

Department

Information Technology

Degree Program

B.Sc. (Hons) in Information Technology

Project Code

R25-012

Research Values & Approach

1

User-Centered Design

Developing with and for visually impaired individuals, incorporating feedback at every stage to ensure real-world usability.

2

Privacy First

All AI processing occurs on-device using Edge AI, ensuring user data remains private and secure without cloud dependency.

3

Accessibility & Affordability

Creating cost-effective solutions (~Rs. 70,000) that are 10x cheaper than commercial alternatives, accessible to developing regions.

Collaborative Research

This research project represents a collaborative effort where each team member developed a specialized component that integrates seamlessly into the unified IoT Smart Glasses system. The modular architecture allows each component to function independently while contributing to the comprehensive assistive solution.

Research Duration

8 months (2024-2025)

Total Components

4 integrated modules

Technologies Used

TinyML, Edge AI, TensorFlow Lite

Target Platform

Raspberry Pi 5 + Android

Connect With Our Team

Interested in our research? Have questions about the technology or collaboration opportunities? We welcome inquiries from researchers, industry partners, and organizations working in assistive technology.