carp_mobile_sensing 1.3.0

Mobile Sensing Framework for Flutter. A software framework for collecting sensor data from the phone and attached wearable devices via probes. Can be extended.

CARP Mobile Sensing Framework in Flutter

This library contains the core Flutter package for the CARP Mobile Sensing (CAMS) framework. Supports cross-platform (iOS and Android) mobile sensing.

For an overview of all CAMS packages, see CARP Mobile Sensing in Flutter. For documentation on how to use CAMS, see the CAMS wiki.

Usage

To use this plugin, add carp_mobile_sensing as dependencies in your pubspec.yaml file.

dependencies:
  flutter:
    sdk: flutter
  carp_core: ^latest
  carp_mobile_sensing: ^latest

Configuration

When you want to add CAMS to you app, there are a few things to do in terms of configuring your app.

First, since CAMS rely on the awesome_notifications plugin, you should configure your app following their configuration guide for both Android and iOS.

Android Integration

Set the minimum android SDK to 23 and Java SDK Version to 33 by setting the minSdkVersion, the compileSdkVersion, and targetSdkVersion in the build.gradle file, located in the android/app/ folder:

android {
    compileSdkVersion 33

    defaultConfig {
        minSdkVersion 23
        targetSdkVersion 33
        ...
    }
    ...
}

The pedometer (step count) probe needs permission to ACTIVITY_RECOGNITION. Add the following to your app's manifest.xml file located in android/app/src/main:

<uses-permission android:name="android.permission.ACTIVITY_RECOGNITION"/>

iOS Integration

The pedometer (step count) probe uses NSMotion on iOS and the NSMotionUsageDescription needs to be specified in the app's Info.plist file located in ios/Runner:

<key>NSMotionUsageDescription</key>
<string>Collecting step count.</string>

For notification to work in background mode, you need to configure your iOS app to be set up for background actions.

---

NOTE: Other CAMS sampling packages require additional permissions in the manifest.xml or Info.plist files. See the documentation for each package.

Documentation

The Dart API doc describes the different libraries and classes.

The CAMS wiki contains detailed documentation on the CARP Mobile Sensing Framework, including the domain model, how to use it by create a study configuration, how to extend it, and an overview of the available measure types.

More scientific documentation of CAMS is available in the following papers:

• Bardram, Jakob E. "The CARP Mobile Sensing Framework--A Cross-platform, Reactive, Programming Framework and Runtime Environment for Digital Phenotyping." arXiv preprint arXiv:2006.11904 (2020). [pdf]
• Bardram, Jakob E. "Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing." Sensors 22.7 (2022). [pdf].

@article{bardram2020carp,
  title={The CARP Mobile Sensing Framework--A Cross-platform, Reactive, Programming Framework and Runtime Environment for Digital Phenotyping},
  author={Bardram, Jakob E},
  journal={arXiv preprint arXiv:2006.11904},
  year={2020}
}

@article{bardram2022software,
  title={Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing},
  author={Bardram, Jakob E},
  journal={Sensors},
  volume={22},
  number={7},
  year={2022},
  publisher={MDPI}
}

Please use these references in any scientific papers using CAMS.

Examples of configuring and using CAMS

There is a very simple example app app which shows how a study can be created with different tasks and measures. This app just prints the sensing data to a console screen on the phone. There is also a range of different examples on how to create a study to take inspiration from.

However, the CARP Mobile Sensing App provides a MUCH better example of how to use the framework in a Flutter BLoC architecture, including good documentation of how to do this.

Below is a small primer in the use of CAMS for a very simple sampling study running locally on the phone. This example is similar to the example app app.

Following carp_core, a CAMS study can be configured, deployed, executed, and used in different steps:

1. Define a SmartphoneStudyProtocol.
2. Deploy this protocol to the SmartPhoneClientManager.
3. Use the generated data (called measurements) locally in the app or specify how and where to store or upload it using a DataEndPoint.
4. Control the execution of the study, like calling start.

Defining a SmartphoneStudyProtocol

In CAMS, a sensing protocol is configured in a SmartphoneStudyProtocol. Below is a simple example of how to set up a protocol that samples step counts, ambient light, screen events, and battery events.

final phone = Smartphone();
final protocol = SmartphoneStudyProtocol(
  ownerId: '[e-mail]',
  name: 'Tracking steps, light, screen, and battery',
  dataEndPoint: SQLiteDataEndPoint(),
)
  ..addPrimaryDevice(phone)
  ..addTaskControl(
    DelayedTrigger(delay: const Duration(seconds: 10)),
    BackgroundTask(measures: [
      Measure(type: SensorSamplingPackage.STEP_COUNT),
      Measure(type: SensorSamplingPackage.AMBIENT_LIGHT),
      Measure(type: DeviceSamplingPackage.SCREEN_EVENT),
      Measure(type: DeviceSamplingPackage.BATTERY_STATE),
    ]),
    phone,
    Control.Start,
  );

The above example defines a simple SmartphoneStudyProtocol which will use a Smartphone as a primary device for data collection and store data in a SQLite database locally on the phone using a SQLiteDataEndPoint. Sampling is configured by adding a TaskControl to the protocol using an DelayedTrigger which triggers a BackgroundTask containing four different Measures. When this task control is triggered (after a delay of 10 seconds), the sampling will start.

Sampling can be configured in very sophisticated ways, by specifying different types of devices, task controls, triggers, tasks, measures, and sampling configurations. See the CAMS wiki for an overview and more details.

Deploying and Running a Study on a SmartPhoneClientManager

In CAMS, we talk about a study protocol being 'deployed' on a primary device, like a phone. CAMS has a fairly sophisticated software architecture for doing this. However, if we want to define and deploy a study locally on the phone, this can be done using the SmartPhoneClientManager singleton.

// Create and configure a client manager for this phone.
await SmartPhoneClientManager().configure();

// Create a study based on the protocol.
SmartPhoneClientManager().addStudyProtocol(protocol);

/// Start sampling.
SmartPhoneClientManager().start();

In this example, the client manager is configured, the protocol is added, and sampling is started. This can actually be done in one line of code, like this:

SmartPhoneClientManager().configure().then((_) => SmartPhoneClientManager()
    .addStudyProtocol(protocol)
    .then((_) => SmartPhoneClientManager().start()));

This will start the sampling, as specified in the protocol, and data is stored in the database.

Using the generated data

The generated data can be accessed and used in the app. Access to data is done by listening on the measurements stream from the client manager, like this:

// Listening on the data stream and print them as json.
SmartPhoneClientManager()
    .measurements
    .listen((measurement) => print(toJsonString(measurement)));

Note that measurements is a Dart Stream and you can hence apply all the usual stream operations to the collected measurements, including sorting, mapping, reducing, and transforming measurements.

Controlling the sampling of data

The execution of sensing can be controlled on runtime by starting, stopping, and disposing sampling. For example, calling SmartPhoneClientManager().stop() would stop the study running on the client. Calling start() would (re)start it again.

Calling SmartPhoneClientManager().dispose() would dispose of the client manager. Once dispose is called, you cannot call start or stop anymore. This methods is typically used in the Flutter dispose() method.

Extending CAMS

CAMS is designed to be extended in many ways, including adding new sampling capabilities by implementing a SamplingPackage, adding a new data management and backend support by creating a DataManager, and creating data and privacy transformer schemas that can transform CARP data to other formats, including privacy protecting them, by implementing a TransformerSchema.

For example, you can write your own DataEndPoint definitions and a corresponding DataManager class for uploading data to your own data endpoint. See the wiki on how to add a new data manager.

Please see the wiki on how to extend CAMS.

Features and bugs

Please read about existing issues and file new feature requests and bug reports at the issue tracker.

License

This software is copyright (c) Copenhagen Center for Health Technology (CACHET) at the Technical University of Denmark (DTU). This software is available 'as-is' under a MIT license.