DementiaGuard LogoDementiaGuard
DementiaGuard Logo

Empowering Dementia Care with Innovation

Welcome to DementiaGuard, your trusted companion in dementia care and support. Designed to enhance the quality of life for dementia patients and their caregivers, our app offers cutting-edge tools and compassionate assistance. From early dementia prediction using MRI brain imaging and a voice-activated therapy assistant to cognitive exercises and geo-fencing for added safety, DementiaGuard combines science and technology to deliver a personalized care experience. Start your journey with DementiaGuard and discover a world where advanced care meets compassionate support.

Introduction

DementiaGuard is a smart solution for supporting dementia patients, developed by a four-member research team. Our app is implemented to enhance cognitive health and improve the quality of life for individuals affected by Alzheimer's Disease by integrating personalized Cognitive Health Improvement Activities, an innovative Voice Enabled Therapy Assistant, Geo-Location Fencing with security measurements, and advanced MRI analysis for medical personals.

Cognitive Enhancement

Personalized cognitive exercises designed to stimulate brain function and slow cognitive decline in dementia patients.

Safety & Security

Advanced geo-fencing technology to ensure patient safety with real-time monitoring and alerts for caregivers.

Compassionate Care

AI-driven voice therapy assistant providing emotional support and reminiscence therapy to improve quality of life.

Research Gaps

Our research identified several critical gaps in existing dementia care solutions that DementiaGuard aims to address through innovative technology.

Lack of Interactive Therapy

Current solutions lack personalized, interactive therapy options for dementia patients.

Inadequate Comprehensive Monitoring

Existing monitoring systems fail to provide comprehensive indoor and outdoor tracking for patient safety.

Non-Personalized Cognitive Exercises

Generic cognitive exercises that don't adapt to individual patient needs and progression.

Fragmented MRI Analysis and Treatment Planning

Lack of integrated systems for MRI analysis and personalized treatment planning for medical professionals.

Research Problems & Solutions

Our research investigates key challenges in Alzheimer’s Disease management and presents innovative technological solutions to enhance patient care and support for caregivers.

Research Problems

Alzheimer’s Disease (AD) management presents numerous challenges across therapy, diagnosis, cognitive support, and safety. Non-pharmacological therapies such as Reminiscence Therapy (RT) are proven to enhance emotional well-being, but access to trained professionals is often limited, placing an additional burden on already overwhelmed caregivers. Diagnostic methods rely heavily on subjective cognitive assessments, which are unreliable in detecting early stages of AD, while deep learning-based MRI analysis struggles with data scarcity and generalizability. Cognitive training exercises lack personalization, making them less effective in addressing individual needs of patients. Furthermore, patient safety remains a significant concern due to the tendency of AD patients to wander, and existing monitoring systems lack accurate indoor tracking and timely alert mechanisms. These fragmented approaches fail to provide a unified, effective care solution for AD patients and their caregivers.

Key Issues

  • Inconsistent access to therapy
  • Subjective and unreliable diagnosis
  • Generic cognitive training tools
  • Inadequate patient safety tracking

Consequences

  • Emotional and cognitive deterioration
  • Misdiagnosis and delayed intervention
  • Ineffective cognitive stimulation
  • Increased risk of patient harm

Research Objectives

Our research aims to address the challenges faced by dementia patients and their caregivers through innovative technological solutions. The following objectives guide our work:

  • Develop an AI-driven voice-enabled therapy assistant to provide personalized reminiscence therapy and emotional support for dementia patients

  • Create a comprehensive cognitive health improvement system with adaptive exercises to slow cognitive decline

  • Implement a geo-location fencing system for enhanced patient safety using IoT technologies

  • Design an advanced MRI analysis system for early detection and personalized treatment planning

  • Integrate all components into a unified platform accessible to patients, caregivers, and medical professionals

Methodology

This research aims to provide a smart solution by integrating a web and mobile platform tailored to the needs of doctors, patients, and caregivers. The system provides an interactive web interface for doctors, enabling real-time patient monitoring, MRI report analysis, and generating personalized treatment plans. Additionally, the mobile application supports patients and caregivers through AI-based therapy assistance, cognitive health improvement activities, and geolocation fencing for enhanced safety. The infrastructure is managed using services offered by Firebase with Google Cloud Platform (GCP) for storage, artifact registry, hosting, databases and etc. ensuring scalability, security, and seamless data management. Using the tools available, caregivers can be involved in monitoring and supporting patients' progress, fostering a team approach to care.

Voice-Enabled Therapy Assistant

Our AI-driven Voice-Enabled Therapy Assistant provides personalized reminiscence therapy. Caregivers feed patient data, such as life stories and photos, which we use to generate tailored therapy questions. Using natural language processing, the assistant engages patients in adaptive conversations, tracks progress, and generates session reports to monitor cognitive health.

Key Features

  • Natural language processing
  • Personalized conversation topics
  • Adaptive difficulty levels
  • Progress tracking and reporting

Benefits

  • Enhanced emotional well-being
  • Improved memory recall
  • Reduced feelings of isolation
  • Cognitive stimulation

Technologies Used

Our research leverages a diverse stack of cutting-edge technologies to deliver a comprehensive solution for dementia care.

Python
Python
React
React
Node.js
Node.js
TensorFlow
TensorFlow
Firebase
Firebase
Google Colab
Google Colab
MongoDB
MongoDB
Docker
Docker
FastAPI
FastAPI
Kotlin
Kotlin
Google Cloud
Google Cloud
Google Maps APIs
Google Maps APIs

Milestones

Track our research project's progress through key milestones and deliverables.

Project Proposal

July 2024

A Project Proposal is presented to potential sponsors or clients to receive funding or get your project approved.

Marks Allocated: 6
Completed
Marks7%

Progress Presentation I

December 2024

Progress Presentation I reviews the 50% completion status of the project. This reveals any gaps or inconsistencies in the design/requirements.

Marks Allocated: 6
Completed
Marks14%

Research Paper

March 2025

Describes what you contribute to existing knowledge, giving due recognition to all work that you referred in making new knowledge.

Marks Allocated: 10
Completed
Marks26%

Progress Presentation II

March 2025

Progress Presentation II reviews the 90% completion status demonstration of the project. Along with a Poster presentation which describes the project as a whole.

Marks Allocated: 18
Completed
Marks48%

Website Assessment

May 2025

The Website helps to promote our research project and reveals all details related to the project.

Marks Allocated: 2
Completed
Marks50%

Final Report

May 2025

Final Report evaluates the completed project done throughout the year. Marks mentioned below includes marks for individual & group reports and also Final report.

Marks Allocated: 19
Completed
Marks73%

Final Presentation & Viva

May 2025

Viva is held individually to assess each member's contribution to the project.

Marks Allocated: 20
Upcoming
Marks73%

Logbook

June 2025

Status of the project is validated through the Logbook. This also includes, Status documents 1 & 2.

Marks Allocated: 3
Upcoming
Marks73%

Documents

Access our research documents, proposals, and reports.

Topic Assessment
DementiaGuard

Submitted On 2024/07/01


Avatar 1Avatar 1Avatar 1Avatar 1
Group Work
Project Proposal
Voice Enabled Theraphy Assistant

Submitted On 2024/07/01


Avatar 1Jayaweera J. A. V. T.
Project Proposal
Dementia Prediction

Submitted On 2024/07/01


Avatar 1Ranasinghe R. A. H. B.
Project Proposal
Cognitive Helth Improving Activities

Submitted On 2024/07/01


Avatar 1Perera K. A. S. N.
Project Proposal
Geolocation Fencing

Submitted On 2024/07/01


Avatar 1Shabeer M. S. M.
Research Paper
DementiaGuard

Awaiting Submission


Avatar 1Avatar 1Avatar 1Avatar 1
Group Work
Final Report
DementiaGuard

Awaiting Submission


Avatar 1Avatar 1Avatar 1Avatar 1
Group Work
Final Report
Voice Enabled Theraphy Assistant

Awaiting Submission


Avatar 1Jayaweera J. A. V. T.
Final Report
Dementia Prediction

Awaiting Submission


Avatar 1Ranasinghe R. A. H. B.
Final Report
Cognitive Helth Improving Activities

Awaiting Submission


Avatar 1Perera K. A. S. N.
Final Report
Geolocation Fencing

Awaiting Submission


Avatar 1Shabeer M. S. M.
Leaflet
DementiaGuard

Awaiting Submission


Avatar 1Avatar 1Avatar 1Avatar 1
Group Work

Presentations

Access our research presentations and slides.

Project Proposal

Submitted On 2024/07/01


Progress Presentation I

Awaiting Submission


Progress Presentation II

Awaiting Submission


Final Presentation

Awaiting Submission


About Us

Meet our dedicated team of researchers and supervisors working on the DementiaGuard project.

Ms. Sanjeevi Chandrasiri

Ms. Sanjeevi Chandrasiri

Supervisor

Senior Lecturer

At SLIIT

Department

Information Technology

Dr. Dharshana Kasthurirathna

Dr. Dharshana Kasthurirathna

Co-Supervisor

Assistant Professor

At SLIIT

Department

Computer Science

Jayaweera J. A. V. T.

Jayaweera J. A. V. T.

Group Leader

Undergraduate Student

At SLIIT

Department

Software Engineering

Ranasinghe R. A. H. B.

Ranasinghe R. A. H. B.

Group Member

Undergraduate Student

At SLIIT

Department

Software Engineering

Perera K. A. S. N.

Perera K. A. S. N.

Group Member

Undergraduate Student

At SLIIT

Department

Software Engineering

Shabeer M. S. M.

Shabeer M. S. M.

Group Member

Undergraduate Student

At SLIIT

Department

Software Engineering

Contact Us

We'd love to hear from you! Please fill out the form below.

Email

vidushatjayaweera@gmail.com

Send Email

Phone

+94 76 132 9104

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Location

SLIIT, New Kandy Road, Malabe, Sri Lanka

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