Adaptive Intensity System with Hyperate Integration

Welcome to our Adaptive Intensity System with real-time integration to the Hyperate API. This system is designed to adjust game or simulation intensity based on real-time heart rate data, providing an immersive experience for the user.

Prerequisites

• Unity 2019.4.18f1 or higher.
• Microsoft.ML 1.5.5 or higher.
• Hyperate WebSocket Token. Get it here.

AdaptiveBPM.ML and AdaptiveBPM.Unity Projects

We have two different projects:

1. AdaptiveBPM.ML:

This project generates machine learning models and tests them in isolation. It focuses on data analysis and model training.

Key Aspects:

• Model Generation: Responsible for generating machine learning models using historical heart rate data.
• Model Testing: Provides a controlled environment to test the accuracy of these models.

2. AdaptiveBPM.Unity:

The Unity project that writes data to the data file and uses the new model for the next prediction of intensity. It's the front-end that integrates with Hyperate and adapts game intensity based on real-time heart rate data.

Key Aspects:

• Integration with Hyperate: Establishes a WebSocket connection with Hyperate to receive real-time heart rate data.
• Intensity Adjustment: Uses the machine learning models generated by AdaptiveBPM.ML to adjust game intensity in real-time.
• Hyperate Data Logging: Writes heart rate data to a file for future use in AdaptiveBPM.ML.

Getting Started

Running the ML Project

• Ensure you have Microsoft.ML installed as a nuget package.
• Run the project. It will generate a new model from csv data and save it to the Models folder.

void RunModel()
{
    // Create a reference to the model
    AdaptiveBpmMLModel mlModel = new AdaptiveBpmMLModel();
    // transfrom and classify the data, save the model
    mlModel.LoadModel();
    // make a prediction
    mlModel.Predict();
}

Running the Unity Simulation and Adding Data

• Ensure you have a WebSocket token from Hyperate and plug it into the hyperateSocket component.
• Attach the AdaptiveBPM script to a GameObject. This object will handle the intensity adjustments.
• Attach the hyperateSocket script to a separate GameObject and reference the AdaptiveBPM object.
• Configure parameters like max/min BPM, history length, etc., according to your requirements.
• Run the simulation or game. Heart rate updates will be received from Hyperate, and intensity adjustments will be made accordingly.
• Add data to the model for prediction refinement

void AddDataToModel()
{
    // Game captured BPM data
    var gameCaptureBPMData = new AdaptiveBpmMLTrainingModel.ModelSerialized { Intensity = 1, BPM = 99, TargetBPM = 110, BPMDifference = 10, Label = 1 };
    // Create a reference to the model
    AdaptiveBpmMLModel model = new AdaptiveBpmMLModel();
    // Append the data to the CSV file
    model.AppendDataToCSV(gameCaptureBPMData);
}

Authors

• Caleb Vaccaro