Blinkenlights

Alzheimer's Disease (AD) is the leading cause of cognitive decline in the western world, affecting a third of Americans over age 85. The disease slowly degrades memory and cognitive skills, hindering the ability to carry out daily activities, inevitably leading to full-time care and death.

Blinkenlights is a AR assistant that accompanies individuals with AD throughout their disease progression to have a better quality of life by promoting independence, assisting with memory recall and encouraging social interaction.

Features

Temporal & Spatial Reminder System

Provides users with temporal or location-tagged reminders to assist with day-to-day activities.

Facial Recognition & Memory Recall (Reminiscence)

Uses facial recognition to help users identify stored people and pulls up shared videos and images for user to review and trigger memory recall of relationship.

Engagement platform for team of caretakers

Provide platform for remote family members and caretakers to engage in care for the individual by sending reminders, adding events to calendar and send voice memos, and monitor user's task progress.

40 Hz Therapy

State-of-art research in Alzheimer's disease has shown that emitting light and playing sound at 40 Hz frequency can drastically reduce beta-amyloid plaques in various areas of the brain in mice models of Alzheimer's Disease, thereby improving ability to perform memory-related tasks. Although findings have not yet shown proven that it is effective in humans, we incorporated this feature to the end-of-day routine as a way to explore the efficacy of this research finding.

Read below for relevant information on research findings: http://news.mit.edu/2019/brain-wave-stimulation-improve-alzheimers-0314

Dependencies

The following dependencies are required.

Installation procedure for OpenCV for Unity with Magic Leap

  1. Create a new project in Unity 2019.2.0b10
  2. Import the v0.22 MagicLeap SDK
  3. Allow unsafe code to enable use of OpenCV assemblies
  4. Import the OpenCV for Unity package from asset store into your project
  5. Complete setup for OpenCV for Unity and Set Plugin Import Settings on the Tools menu under OpenCV for Unity
  6. Move “OpenCVForUnity/StreamingAssets/” folder to the Unity's program's “Assets/” folder
  7. Provide privileges for camera usage, microphone usage and Computer Vision usage on the LuminSDK publishing settings
  8. Ensure the front camera lights are lit up to ensure camera usage is functional

Research

We conducted secondary research and interviewed 3 family members of Alzheimer patients, along with two experts (neurologist & PhD candidate in Gerontology and Dementia studies) to better understand the problem space and identify user needs.

Interview Questions

Key Findings

  1. Memory loss interferes with daily tasks such as medication compliance, turning off burner, and self care > "She would...forget how many pills to take and so we had to monitor the pills" - S.O. on her mother

"Hard time with personal care/grooming" - M.T. on his father

  1. Social interaction slows down disease progression and helps the patients feel happier > "Loneliness is a risk factor for depression which can cause a quicker decline. They would not take as good care of themselves if they're also experiencing depression. There is total loss of motivation to do so." - _AS, Corporate Director of Dementia Programs at Tutera Senior Living and Health Care _


"More visits from family, people who knew him and loved him [would help him feel happier and independent]" - M.T. on his father

  1. Photos and sounds are helpful in reminiscence therapy to help patients trigger memories > "We really rely on their family photos. A lot of times, people will look at photos and go through them to make sure they still remember in early stages. Photos are good at sparking memory." - _A.S.

Process

Brainstorming

Problem Statement

Journey Mapping

User Personas + Flows

Design Elements

Disclosure about the use of the OpenCV for Unity paid asset:

The use of the paid asset was permitted by organizers and Magic Leap team to help us efficiently bootstrap the development of the facial detection/recognition module in our project at 10pm EST on 18th Jan 2020 due to significant difficulties the teams faced which trying to include OpenCV with magic leap.

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