mobility_features 2.0.0

Real-time mobility feature calculation

Mobility Features

Author: Thomas Nilsson ([e-mail])

Usage

Initial setup

Add the package to your pubspec.yaml file and import the package

No permissions are required to use the package, however, a location plugin should be used to stream data.

We recommend the plugin https://pub.dev/packages/mubs_background_location which works on both Android and iOS as of July 2020.

import 'package:mobility_features/mobility_features.dart';

Step 1: Init the MobilityFactory instance

MobilityFactory mobilityFactory = MobilityFactory.instance;

Optionally, the following configurations can be made, which will influence the algorithms for producing features.

In general the stop radius should be kept low (5-20 meters) and the place radius somewhat higher (25-50 meters). Computation of features is triggered when users move around and change their geo-position. Low parameter values will make the features more fine grained but will trigger computation more often.

StreamSubscription<MobilityContext> mobilitySubscription;
MobilityFactory mobilityFactory = MobilityFactory.instance;
MobilityContext _mobilityContext;

void initState() {
    ...
    mobilityFactory.stopDuration = Duration(seconds: 30);
    mobilityFactory.placeRadius = 20;
    mobilityFactory.stopRadius = 5;
}

Step 2: Set up streaming

Location data collection is not directly supported by this package, for this you have to use a location plugin such as https://pub.dev/packages/mubs_background_location.

From here, you can to convert from whichever Data Transfer Object is used by the location plugin to a LocationSample.

Next, you need to subscribe to the MobilityFactory instance's contextStream to be be notified each time a new set of features has been computed.

Below is shown an example using the mubs_background_location plugin, where a LocationDto stream is converted into a LocationSample stream by using a map-function.

/// Start the streaming of location data and mobility features
void streamInit() async {
    /// Get the location data stream (specific to mubs_background_location)
    Stream<LocationDto> dtoStream = locationManager.dtoStream;
    
    /// Start the location service (specific to mubs_background_location)
    await locationManager.start();
    
    /// Convert from [LocationDto] to [LocationSample]
    Stream<LocationSample> locationSampleStream = dtoStream.map((e) =>
        LocationSample(GeoLocation(e.latitude, e.longitude), DateTime.now()));

    /// Provide the MobilityFactory instance with the LocationSample stream
    mobilityFactory.startListening(locationSampleStream);
    
    /// Start listening to incoming MobilityContext objects
    mobilityFactory.contextStream.listen(onMobilityContext);
}

Step 3: Handle features

A call-back method is used to handle incoming MobilityContext objects:

/// Handle incoming contexts
void onMobilityContext(MobilityContext context) {
  /// Do something with the context
  print('Context received: ${context.toJson()}');
}

All features are implemented as getters for a MobilityContext object.

context.places;
context.stops;
context.moves;

context.numberOfPlaces;
context.homeStay;
context.entropy;
context.normalizedEntropy;
context.distanceTravelled;
context.routineIndex;

Note: it is not possible to instantiate a MobilityContext object directly.

Feature-specific instructions

When a feature cannot be evaluated, it will result in a value of -1.0.

The Home Stay feature requires at least some data to be collected between 00:00 and 06:00, otherwise the feature cannot be evaluated.

The Routine Index feature requires at least two days of sufficient data to be computed.

The Entropy and Normalized Entropy features require at least 2 places to be evaluated. If only a single place was found, the feature can technically still be evaluated and will result in an Entropy of 0, as per the definition of Entropy.

Theorical Background

For mental health research, location data, together with a time component, both collected from the user’s smartphone, can be reduced to certain behavioral features pertaining to the user’s mobility. These features can be used to diagnose patients suffering from mental disorders such as depression.

Previously, mobility recognition has been done in an off-device fashion where features are extracted after a study was completed. We propose performing mobility feature extracting in real-time on the device itself, as new data comes in a continuous fashion. This trades compute power, i.e. phone battery for bandwidth and storage since the reduced features take up much less space than the raw GPS data, and transforms the very intrusive GPS data to abstract features, which avoids unnecessary logging of sensitive data.

Location Features

The mobility features which will be used are derived from GPS location data are:

Stop A collection of GPS points which together represent a visit at a known \texit{Place} (see below) for an extended period of time. A \textit{Stop} is defined by a location that represents the centroid of a collection of data points, from which a \textit{Stop} is created. In addition a \textit{Stop} also has an \textit{arrival}- and a \textit{departure} time-stamp, representing when the user arrived at the place and when the user left the place. From the arrival- and departure timestamps of the \textit{Stop} the duration can be computed.

Place A group of stops that were clustered by the DBSCAN algorithm \cite{density-based-1996}. From the cluster of stops, the centroid of the stops can be found, i.e. the center location. In addition, it can be computed how long a user has visited a given place by summing over the duration of all the stops at that place.

Move The travel between two Stops, which the user will pass though a path of GPS points. The distance of a Move can be computed as the sum of using the haversine distance of this path. Given the distance travelled as well as departure and arrival timestamp from the Stops, the average speed at which the user traveled can be derived.

Derived Features

Home Stay The portion (percentage) of the total time elapsed since midnight which was spent at home. Elapsed time is calculated from the departure time of the last known stop.

Location Variance The statistical variance in the latitude- and longitudinal coordinates.

Number of Places The number of places visited today.

Entropy The entropy with respect to time spent at places.

Normalized Entropy The normalized entropy with respect to time spent at places.

Distance Travelled The total distance travelled today (in meters), i.e. not limited to walking or running.

Routine Index The percentage of today that overlapped with the previous, maximally, 28 days.