Design & Functionalities

The targeted users of the MMRTD application are elderly with mild (MMSE score ≥20) and moderate dementia (MMSE score 10 - 19). The app consists of two parts of therapy sessions.

Part one is designed for the patients to practice physical skills in the virtual Victoria Park. Users are asked to complete different activities and movements to coordinate the body, enhance concentration and trigger their memories.

Part two is designed for patients to practice cognitive skills. Users are required to complete several tasks of washing clothes in the laundry room. In the advanced version, when the user reaches the step where they pour in washing powder, a respective scent will be emitted by an emission device attached to the headset.

The entire duration for completing the activities will be less than 5 – 10 minutes, subject to the user's speed and comprehension of the required motions. The user remains seated throughout the activity, making the application suitable for patients with difficulty moving around or walking. The carer can monitor all activities engaged by the user in real-time via a simulcast from the MR headset to a mobile phone, tablet, or computer.

Unique Features

Differentiators of MMRTD

Person-centered design

Create an immersive virtual environment that the user is familiar with.
Match the person's strength, level of ability, and give encouragement and praise (e.g. Victoria Park setting, virtual instructor voice-over by family member)

Integrated therapy approach

Exercises and physiotherapy: maximize upper-extremity, motor activity, body balance, muscle strength.
Multi-sensory environment therapy: stimulating the user’ sense of sight, hearing and touch.
Reminiscence therapy: trigger user’s memory with music, sounds and images.

MR/VR technology

The app uses Unity as the Software Engine, C# as the programming language, and Oculus Quest 2 as the main VR headset.
Intergrated with the perception of visuals, olfactory, and auditory.

Machine learning analytic tool

Analyzes facial expressions and limb action by integrating an open-source model with OpenCV and machine learning.
Enable the developer to assess the effective and enjoyable therapy factor for the elderly patient