geriatric Depression game and app suite
Team Size: 3
Utkarsh Agarawal - Producer
Dickens Chong - Artist
Shantanu Pandey - Engineer
Development Time: Jan 2018 - Apr 2019
Development Engine: Unity
Developed at Therapeutic Games and Apps Lab in collaboration with Dr. Shizuko Morimoto
NEURAL HEALTH NETWORK
The effectiveness of traditional medication drops in the case of Geriatric Depression, due to deterioration in executive functioning, attention bias, and inhibition control. Neural Health Network is scaffolding around a set of brain games (Neurogrow, ThinkPlus, and WordGame) that help solve geriatric depression. It provides access to these games. The network also aims to solve the lack of motivation, isolation, and stigmatization of depression by creating a friendly social engagement for patients to communicate, share thoughts and motivate each other. The app also helps in de-stigmatizing depression by providing facts about depression.
As the engineer , I implemented the following features in this application:
- Daily goals push notification.
- Access to other games through the app.
- Asynchronous controlled messaging including the support of gifs.
- SQL database at the back-end for storing user information and various games data.
- Secure communication (https) with the database using PHP scripts.
Neurogrow, focuses on inhibition control. It trains the patients’ inhibition control thus improving their response to traditional medication. It also exercises their working memory. The game starts with a flower and a can. As the levels progress, two more flowers and cans are introduced. Player has to tap if the correct flower and can combination is displayed. Different gameplay modifiers are introduced at various different stages of the game. Tapping controls are modified to tap left or right, hold or avoid clicking the screen.
Adaptive difficulty is adjustable by the clinician where where different parameters editable via external dashboard define the win-lose states.
We started this medical game app over an existing code base by a different team. The major engineering tasks for this game included:
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- Gameplay re-architecture to support level system and different gameplay modifiers.
- Adaptive difficulty system: Game increase or decreases difficulty based on how the player is performing in a current difficulty.
- Game Analytics: Success streak, accuracy and response time were all saved in the database at an external server.
- Player profiling: Each player has a profile in the database keeping track of the player's level stats and analytics data.
- Secure database communication: Between each level, a secured database.
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Think-Plus, focuses on attention bias. It trains the patients’ attention bias to focus more on the positive. It does so by reward processing techniques. The game plays like an endless where the player has to identify between various images and stay on the lane of the most positive image. The game plays like an endless where the player has to identify between various images and stay on the lane of the most positive image. The game can be tailored for multiple types of categories (like depression, PTSD etc.) and the images displayed in the game are under the control of the clinician.
Various features of this game are as follows:
- Level editor for adding as many levels as required. Any level will be as long as it is uploaded by the clinician.
- A web dashboard for the clinician to upload levels, images and access user analytics data.
- Image/Texture scaling for allowing the clinician to use any image in the game.
- Endless default level if the game couldn't find any level files.
- Unity object pooling for optimization.
- Secure server communication for saving user data and reading game data.