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https://github.com/sobri909/locokit

Location, motion, and activity recording framework for iOS
https://github.com/sobri909/locokit

corelocation coremotion framework gps ios location swift

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Location, motion, and activity recording framework for iOS

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README 

        
# LocoKit



A Machine Learning based location recording and activity detection framework for iOS.



## Location and Motion Recording



- Combined, simplified Core Location and Core Motion recording

- Filtered, smoothed, and simplified location and motion data

- Near real time stationary / moving state detection

- Automatic energy use management, enabling all day recording

- Automatic stopping and restarting of recording, to avoid wasteful battery use



## Activity Type Detection



- Machine Learning based activity type detection

- Improved detection of Core Motion activity types (stationary, walking, running, cycling,

  automotive)

- Distinguish between specific transport types (car, train, bus, motorcycle, airplane, boat)



## Record High Level Visits and Paths



- Optionally produce high level `Path` and `Visit` timeline items, to represent the recording 

  session at human level. Similar to Core Location's `CLVisit`, but with much higher accuracy, much 

  more detail, and with the addition of Paths (ie the trips between Visits). 

- Optionally persist your recorded samples and timeline items to a local SQL based store, for

  retention between sessions.



[More information about timeline items can be found here](https://github.com/sobri909/LocoKit/blob/master/TimelineItemDescription.md)



## Supporting the Project



LocoKit is an LGPL licensed open source project. Its ongoing development is made possible 

thanks to the support of its backers on Patreon.



- [patreon.com/sobri909](https://www.patreon.com/sobri909)



If you have an app that uses LocoKit and is a revenue generating product, please consider

sponsoring LocoKit development, to ensure the project that your product relies on stays

healthy and actively maintained.



Thanks so much for your support!



# Installation



```ruby

pod 'LocoKit'

pod 'LocoKit/LocalStore' # optional

```



**Note:** Include the optional `LocoKit/LocalStore` subspec if you would like to retain your samples

and timeline items in the SQL persistent store.



# High Level Recording 



## Record TimelineItems (Paths and Visits)



```swift

// retain a timeline manager

self.timeline = TimelineManager()



// start recording, and producing timeline items 

self.timeline.startRecording()



// observe timeline item updates

when(timeline, does: .updatedTimelineItem) { _ in

    let currentItem = timeline.currentItem



    // duration of the current Path or Visit

    print("item.duration: \(currentItem.duration)")



    // activity type of the current Path (eg walking, cycling, car)

    if let path = currentItem as? Path {

        print("path.activityType: \(path.activityType)")

    }



    // examine each of the LocomotionSamples within the Path or Visit

    for sample in currentItem.samples {

        print("sample: \(sample)")

    }

}

```



# Low Level Recording



## Record LocomotionSamples (CLLocations combined with Core Motion data)



```swift

// the recording manager singleton

let loco = LocomotionManager.highlander

```



```swift

// decide which Core Motion features to include

loco.recordPedometerEvents = true

loco.recordAccelerometerEvents = true

loco.recordCoreMotionActivityTypeEvents = true

```



```swift

// decide whether to use "sleep mode" to allow for all day recording 

loco.useLowPowerSleepModeWhileStationary = true

```



**Note:** The above settings are all on by default. The above snippets are unnecessary, and just here 

  to show you some of the available options. 



```swift

// start recording 

loco.startRecording()

```



```swift

// watch for updated LocomotionSamples

when(loco, does: .locomotionSampleUpdated) { _ in



    // the raw CLLocation

    print(loco.rawLocation)



    // a more usable, de-noised CLLocation

    print(loco.filteredLocation)



    // a smoothed, simplified, combined location and motion sample

    print(loco.locomotionSample())

}

```



## Fetching TimelineItems / Samples



If you wanted to get all timeline items between the start of today and now, you might do this:



```swift

let date = Date() // some specific day

let items = store.items(

        where: "deleted = 0 AND endDate > ? AND startDate < ? ORDER BY endDate",

        arguments: [date.startOfDay, date.endOfDay])

```



You can also construct more complex queries, like for fetching all timeline items that overlap a certain geographic region. Or all samples of a specific activity type (eg all "car" samples). Or all timeline items that contain samples over a certain speed (eg paths containing fast driving).



## Detect Activity Types



Note that if you are using a `TimelineManager`, activity type classifying is already handled 

for you by the manager, on both the sample and timeline item levels. You should only need to 

directly interact with clasifiers if you are either not using a TimelineManager, or are wanting 

to do low level processing at the sample level.



```swift

// fetch a geographically relevant classifier

let classifier = ActivityTypeClassifier(coordinate: location.coordinate)



// classify a locomotion sample

let results = classifier.classify(sample)



// get the best match activity type

let bestMatch = results.first



// print the best match type's name ("walking", "car", etc)

print(bestMatch.name)

```



**Note:** The above code snippets use [SwiftNotes](https://github.com/sobri909/SwiftNotes) to make

  the event observing code easier to read. If you're not using SwiftNotes, your observers should be

  written something like this:



```swift

let noteCenter = NotificationCenter.default

let queue = OperationQueue.main 



// watch for updates

noteCenter.addObserver(forName: .locomotionSampleUpdated, object: loco, queue: queue) { _ in

    // do stuff

}

```



## Background Location Monitoring



If you want the app to be relaunched after the user force quits, enable significant location change monitoring.



[More details and requirements here](https://github.com/sobri909/LocoKit/blob/master/BackgroundLocationMonitoring.md)



## Examples and Screenshots



- [Location filtering 

  examples](https://github.com/sobri909/LocoKit/blob/master/LocationFilteringExamples.md)

- [Activity type detection examples](https://github.com/sobri909/LocoKit/blob/master/ActivityTypeClassifierExamples.md)



## Documentation 



- [LocoKit API reference](https://www.bigpaua.com/arckit/docs)

- [LocoKitCore API reference](https://www.bigpaua.com/arckit/docs_core)



## Try the LocoKit Demo App



1. Download or clone this repository

2. `pod install`

3. In Xcode, change the Demo App project's "Team" to match your Apple Developer Account

4. In Xcode, change the Demo App project's "Bundle Identifier" to something unique

5. Build and run!

6. Go for a walk, cycle, drive, etc, and see the results :)



## Try Arc App on the App Store



- To see the SDK in action in a live, production app, install

  [Arc App](https://itunes.apple.com/app/arc-app-location-activity-tracker/id1063151918?mt=8) 

  from the App Store, our free life logging app based on LocoKit