VisualR

VisualR is a VR-based smartphone application to perform vision function tests. It consists of a smartphone app that is used together with a simple VR headset.

The app includes novel tests for metamorphopsia, contrast sensitivity and reading speed that can be performed by following simple instructions and providing verbal responses to visual cues, without the need for expert supervision. The smartphone handles all data processing and image display; the app does not require an internet connection, and all data is stored locally and owned by the user. VisualR is currently optimized for iOS phones, particularly the iPhone 13.

More details about VisualR can be found in Sozzi et al. (MedRxiv 2023).

VisualR is a prototype tool for performing visual tests. It is not intended to be used as a medical device or to diagnose, treat, or prevent any disease or condition.

Development

The app was built using Flutter and Unity and makes use of flutter_unity_widget to embed the Unity app as a module into the Flutter application.

Repository structure

All relevant source files for the Flutter application can be found in lib/. The lib directory is divided into the following domains:

Domain	Usage
common/	All classes and services used my multiple parts of the app
contrast/	All classes and services used by the "contrast sensitivity" test
distortion/	All classes and services used by the "distortion" test
ipda/	All classes and services used by the interpupillary distance adjustment
reading_speed/	All classes and services used by the "reading speed" test

Each domain itself is then divided again into the following subdirectories (if applicable):

Directory	Usage
database/	Classes used for storing and accessing data
models/	Classes that describe models used by the respective domain for data storage and processing
providers/	All provider classes
services/	All services commonly used or by the respective visual test

The code for the embedded Unity application can be found in /unity/visualr_unity/. All audio files, fonts and icons used during the visual tests are inside Assets/Artworks/, while the scripts for running and displaying the visual tests are in Assets/Scripts/. Here the scripts are separated into different namespaces depending on their usage, e.g. ArcMinuteCalculation, Contrast or Ui.

Visual test logic

All logic for each visual test resides inside the Flutter application and is similar across all visual tests.

For each visual test there are at least three classes in charge of the logic and data flow:

1. The visual test specific implementation of the abstract class UnityViewPage, which is responsible for rendering the Unity content and sending and receiving messages to and from the Unity module.

2. The visual test service responsible for the communication between the UnityViewPage and the visual test's manager class.

3. The visual test's manager class, which is in charge of creating new test events, keeping track of the user's answers and storing the test results as soon as the test is finished.

For some visual tests there are additional services needed for the creation of test events.

In more detail the flow to create new test events will look like the following:

Inside views/unity/ there is an implementation of the UnityViewPage that makes use of the UnityWidget for each visual test. The UnityWidget is wrapped inside a StreamBuilder which takes care of feeding the Unity module with data for the next test step. When there is a new test step to be created, an event is triggered within the respective service of the visual test, e.g. the ContrastService, which itself will call createNextTestEvent() of the respective manager class, e.g. ContrastManager.

While the logic for creating a new test event will look differently for each visual test, the result will always be that a new event will be added to the stream used for the StreamBuilder widget which will take care of sending the newly created test event to Unity.

An example flow to create a new test step of the contrast test looks like this (although it will be the same for the other visual tests):

UnityContrastPage → ContrastService → ContrastManager → ContrastService → UnityContrastPage

After the test is finished (indicated by createTestEvent() returning null) the manager will take care of creating the test results and storing them in the database.

Building

Before being able to build the application using Xcode or the Flutter CLI, the following steps need to be done.

Getting required assets

The following assets need to be downloaded and added to the repository.

Whisper speech recognition models

VisualR uses Whisper to transcribe audio files. The models used are the multilingual base model and the English base model, that need to be placed in /assets/models. The models can be downloaded in the required format using the download tool provided by whisper.cpp, the C++ port of Whisper.

Vosk speech recognition models

In addition to Whisper, VisualR also relies on the speech recognition engine Vosk for the voice commands inside the app. The app uses the small German model and the small English model, both in version 0.15, but we expect that later versions of the models will work the same way. They can be downloaded from the official list of models compatible with the Vosk-API. Make sure to download vosk-model-small-en-us-0.xx and vosk-model-small-de-0.xx.

The contents of the downloaded .zip files then need to be placed in /unity/visualr_unity/Assets/StreamingAssets/LanguageModels/de-DE and /unity/visualr_unity/Assets/StreamingAssets/LanguageModels/en-US respectively.

Unity assets from the Unity Asset Store

This project relies on some assets from the Unity Asset Store. Most are free-of-charge while others have a cost (indicated by ($)). All assets listed below need to be downloaded from the asset store and added to the unity project.

• Circle Generator (v1.0.2)
• DOTween (HOTween v2) (v1.2.745)
• Lean GUI Shapes (v2.0.2) ($)
• Recognissimo: Offline Speech Recognition (v2.0.6) ($) (*)
• StrangeIoC (v0.7.0)

(*) While VisualR is using the paid asset Recognissimo to do offline speech recognition via Vosk, one might be able to replace it by one of the sample projects provided by AlphaCephei. However, this was not tested and might not work the same way as Recognissimo does.

TextMesh Pro

VisualR uses TextMesh Pro to render text. Even though TextMesh Pro is shipped together with the Unity Editor it still needs to be added to project. This can be done by opening unity/visualr-unity/ in the unity Editor and using the "Window → TextMeshPro → Import TMP Essential Resources" menu option.

Getting Flutter packages

All packages needed to build VisualR can be downloaded by running the following command from the root of the repository:

flutter pub get

Generating needed files

Some files used for the database models and routes of the app need to be generated via build_runner. This can be done by running the following command:

 dart run build_runner build --delete-conflicting-outputs

Export Unity project

In order to build and export the Unity project, one needs to open /unity/visualr_unity/ in the Unity Editor. After setting the build platform to iOS within the Build Settings of the Unity Editor, one can use the "Export iOS" option from the "Flutter" context within the menu bar. This will build the Unity application to ios/UnityLibrary/.

Set Signing & Capabilities

One needs to set the "Signing & Capabilities" within Xcode to their respective development team and (optionally) certificates. In addition, a unique bundle identifier needs to be provided. To do that one needs to open ios/Runner.xcworkspace/ in Xcode, choose "Runner" on the left side menu and navigate to the "Signing & Capabilities" tab.

Install Pods via CocoaPods

Flutter uses CocoaPods, which can either be installed via Ruby

sudo gem install cocoapods

or via brew

brew install cocoapods

After CocoaPods has successfully been installed, one needs to run pod install from within the ios/ directory.