How BodyScan Works
Scan | BodyScan |
Duration | <60 seconds |
Data Returned | Body circumference, Body composition (Body Fat %), Health indicators, like waist-hip ratio, Health risks, like Type-2 Diabetes and Obesity. |
Processing | On-Device |
Platforms | iOS, Android |
iOS submodule size | < 10MB |
Android submodule Size | < 10MB. |
Min. Requirements | iPhones: iOS 12.1 or above. Android: Android 8 or above, 64-bit, and OpenGL 3.1 support. |
Requirements to Operate | Internet connection, Remote Assets, User Input |
Requiring only a function call to launch, our BodyScan is designed to be triggered from anywhere within your app. Please note that V22.0 has undergone a complete overhaul of the user experience with realtime guidance and feedback to enhance the user experience. Below is a typical journey a user will go through to access the scan and subsequently their scan results, consisting of:
BodyScan Requirements
There are some important aspects that need to be considered prior to triggering the BodyScan. See Body Scan Requirements.
Behind The Scenes
Image inspection utilizes our machine learning algorithms to analyze the images captured, ensuring there is a face, hands, feet and that they are in the correct location. Our machine learning models are running in real time directly from the camera feed to ensure the user is standing in the correct position and pose.
Ensure the user's phone is placed perpendicular and in the correct position and orientation to take a scan. The SDK will prompt the user with cues which will help to guide the user to the correct position. After the user has successfully aligned their device, the front and side image capture process will begin. Examples of cues are as follows:
Once the body is aligned accordingly, the process of the front image capture will begin:
Once this is successfully captured, the user will be prompted to begin the side capture process:
Similarly to the front capture, once the side profile is aligned, the process of capturing the side image profile will begin:
If the scan is successful, the user will be altered of this on the following screen:
Behind The Scenes
We process the images on the device using hardware accelerated machine learning models. This means no images ever leave the device.
