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FriendInNeed 0.1.1

3.2 MB / 0+ Downloads / Rating 1.0 - 1 reviews


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FriendInNeed, developed and published by Kozyrev Sergii, has released its latest version, 0.1.1, on 2016-10-02. This app falls under the Health & Fitness category on the Google Play Store and has achieved over 10 installs. It currently holds an overall rating of 1.0, based on 1 reviews.

FriendInNeed APK available on this page is compatible with all Android devices that meet the required specifications (Android 4.3+). It can also be installed on PC and Mac using an Android emulator such as Bluestacks, LDPlayer, and others.

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App Screenshot

App Screenshot

App Details

Package name: com.friendinneed.ua.friendinneed

Updated: 8 years ago

Developer Name: Kozyrev Sergii

Category: Health & Fitness

App Permissions: Show more

Installation Instructions

This article outlines two straightforward methods for installing FriendInNeed on PC Windows and Mac.

Using BlueStacks

  1. Download the APK/XAPK file from this page.
  2. Install BlueStacks by visiting http://bluestacks.com.
  3. Open the APK/XAPK file by double-clicking it. This action will launch BlueStacks and begin the application's installation. If the APK file does not automatically open with BlueStacks, right-click on it and select 'Open with...', then navigate to BlueStacks. Alternatively, you can drag-and-drop the APK file onto the BlueStacks home screen.
  4. Wait a few seconds for the installation to complete. Once done, the installed app will appear on the BlueStacks home screen. Click its icon to start using the application.

Using LDPlayer

  1. Download and install LDPlayer from https://www.ldplayer.net.
  2. Drag the APK/XAPK file directly into LDPlayer.

If you have any questions, please don't hesitate to contact us.

App Rating

1.0
Total 1 reviews

Previous Versions

FriendInNeed 0.1.1
2016-10-02 / 3.2 MB / Android 4.3+

About this app

We use unique ML algorithm with sensors data to decide if fall happen.
Application collects sensors (accelerometer and gyroscope) data and checks if acceleration reached fall threshold. Then application prepare sensor data for last two seconds (max duration of fall) and sends request to backend. On he backend we have Flask python service with TensorFlow for Machine Learning. Currently we use Deep Neural Network Classifier which gives us accuracy of approximately 80%. So the backend side can decide in more precise and accurate way if the person actually fell to the ground and needs help. Application gets response and now knows it should notify list of predefined and chosen friends by alarm message containing GPS coordinates and call for help. We also have some inactivity period, after application got response from server and before it actually sends alarm messages - it notifies user fall detected and shows timer counter which could be cancelled with button or any significant move. In both cases we also redeliver sensor data to server but this time in labeled format (because user marks it as false positive in case she cancels countdown timer) - and this way we collect data to retrain network on daily basis.

App Permissions

Allows an app to access approximate location.
Allows an app to access precise location.
Allows an app to create windows using the type TYPE_APPLICATION_OVERLAY, shown on top of all other apps.
Allows an application to write to external storage.
Allows an application to read from external storage.
Allows access to the vibrator.
Allows applications to open network sockets.
Allows an application to send SMS messages.
Allows an application to read the user's contacts data.
Allows applications to access information about networks.