carp_mobile_sensing 0.20.0

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 a dependency in your pubspec.yaml file.

Android Integration

Add the following to your app's manifest.xml file located in android/app/src/main:

<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="<your_package_name"
    xmlns:tools="http://schemas.android.com/tools">

   ...
   
    <!-- The following permissions are used for CARP Mobile Sensing -->
    <uses-permission android:name="android.permission.PACKAGE_USAGE_STATS" tools:ignore="ProtectedPermissions"/>
    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
    <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION" />

</manifest>

NOTE: Other CAMS sampling packages require additional permissions in the manifest.xml file. See the documentation for each package.

NOTE: Version 0.5.0 is migrated to AndroidX. It requires any Android apps using this plugin to also

migrate if they're using the original support library. See Flutter AndroidX compatibility

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>

Documentation

The Dart API doc describes the different libraries and classes.

The 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 different Measure types available.

A more scientific documentation of CAMS is available at arxiv.org:

• 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]

@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}
}

Please use this as a reference 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 package in a Flutter BLoC architecture, including good documentation of how to do this.

However, below is a small primer in the use of CAMS.

Following carp_core, a CAMS study is created in three steps:

1. Define a StudyProtcol.
2. Deploy this protocol to a DeploymentService.
3. Start executing the study deployment on the phone using a StudyDeploymentController.
4. Use the generated data locally in the app.

Defining a StudyProtcol

In CAMS, a sensing protocol is configured in a CAMSStudyProtocol. Below is a simple example of how to set up a protocol that sense step counts (pedometer), ambient light (light), screen activity (screen), and power consumption (battery). This data is stored as json to a local file on the phone.

// Import package
import 'package:carp_core/carp_core.dart';
import 'package:carp_mobile_sensing/carp_mobile_sensing.dart';

void example_1() async {
  // create a protocol using a local file to store data
  CAMSStudyProtocol protocol = CAMSStudyProtocol()
    ..name = 'Track patient movement'
    ..owner = ProtocolOwner(
      id: 'AB',
      name: 'Alex Boyon',
      email: '[e-mail]',
    )
    ..dataEndPoint = FileDataEndPoint(
      bufferSize: 500 * 1000,
      zip: true,
      encrypt: false,
    );

  // define which devices are used for data collection
  // in this case, its only this smartphone
  Smartphone phone = Smartphone();
  protocol.addMasterDevice(phone);

  // Add an automatic task that immediately starts collecting
  // step counts, ambient light, screen activity, and battery level
  protocol.addTriggeredTask(
      ImmediateTrigger(),
      AutomaticTask()
        ..addMeasures(SensorSamplingPackage().common.getMeasureList(
          types: [
            SensorSamplingPackage.PEDOMETER,
            SensorSamplingPackage.LIGHT,
          ],
        ))
        ..addMeasures(DeviceSamplingPackage().common.getMeasureList(
          types: [
            DeviceSamplingPackage.SCREEN,
            DeviceSamplingPackage.BATTERY,
          ],
        )),
      phone);
}

The above example make use of the pre-defined SamplingSchema named common. This sampling schema contains a set of default settings for how to sample the different measures.

Sampling can be configured in a very sophisticated ways, by specifying different types of triggers, tasks, and measures - see the CAMS domain model for an overview.

Using a DeploymentService

A StudyProtocol can be deployed to a DeploymentService which handles the deployment of protocols for different devices. CAMS comes with a very simple deployment service which runs locally on the phone. This can be used to deploy a protocol and get back a MasterDeviceDeployment, which can be executed on the phone.

  ...

  // deploy this protocol using the on-phone deployment service
  StudyDeploymentStatus status =
      await CAMSDeploymentService().createStudyDeployment(protocol);

  // now ready to get the device deployment configuration for this phone
  CAMSMasterDeviceDeployment deployment = await CAMSDeploymentService()
      .getDeviceDeployment(status.studyDeploymentId);

  ...

Running a StudyDeploymentController

When we have a master device deployment for the phone, this deployment can be excuted and sensing is started. Sensing is controlled by a StudyDeploymentController.

  ...

  // Create a study deployment controller that can manage this deployment
  StudyDeploymentController controller = StudyDeploymentController(deployment);

  // initialize the controller and resume sampling
  await controller.initialize();
  controller.resume();

  // listening and print all data events from the study
  controller.data.forEach(print);

  ...

Using the generated data

Sensing can be controlled in a number of ways and the generated data can be assessed and used in the app. Access to data is done by listening in on the data streams from the study deployment controller or some of its underlying executors or probes. Below are a few examples on how to listen on data streams.

  ...

  // listening to the stream of all data events from the controller
  controller.data.listen((dataPoint) => print(dataPoint));

  // listen only on CARP events
  controller.data
      .where((dataPoint) => dataPoint.data.format.namespace == NameSpace.CARP)
      .listen((event) => print(event));

  // listen on LIGHT events only
  controller.data
      .where((dataPoint) =>
          dataPoint.data.format.toString() == SensorSamplingPackage.LIGHT)
      .listen((event) => print(event));

  // listening on the data generated from all probes 
  // this is equivalent to the statement above
  ProbeRegistry()
      .eventsByType(SensorSamplingPackage.LIGHT)
      .listen((dataPoint) => print(dataPoint));

  // map events to JSON and then print
  controller.data
      .map((dataPoint) => dataPoint.toJson())
      .listen((event) => print(event));

  // subscribe to the stream of data
  StreamSubscription<DataPoint> subscription =
      controller.data.listen((DataPoint dataPoint) {
    // do something w. the datum, e.g. print the json
    print(JsonEncoder.withIndent(' ').convert(dataPoint));
  });

  ...

The execution of sensing can be controlled on runtime. For example:

  ...

  // sampling can be paused and resumed
  controller.pause();
  controller.resume();

  // pause specific probe(s)
  ProbeRegistry()
      .lookup(SensorSamplingPackage.ACCELEROMETER)
      .forEach((probe) => probe.pause());

  // adapt measures on the go - calling hasChanged() force a restart of
  // the probe, which will load the new measure
  lightMeasure
    ..frequency = const Duration(seconds: 12)
    ..duration = const Duration(milliseconds: 500)
    ..hasChanged();

  // disabling a measure will pause the probe
  lightMeasure
    ..enabled = false
    ..hasChanged();

  // once the sampling has to stop, e.g. in a Flutter dispose() methods, call stop.
  // note that once a sampling has stopped, it cannot be restarted.
  controller.stop();
  subscription.cancel();

  ...

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.