BodyScan provides near-instant and accurate body composition and dimension data using your smartphone. With BodyScan, you can accurately and comfortably self-report composition and dimension data in the comfort and privacy of your own home. BodyScan employs patented machine learning algorithms to predict and determine key health indicators associated with obesity and diabetes. Validated through clinical trials with over 7000 participants, BodyScan empowers individuals to take control of their health and make informed decisions.

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How is BodyScan performed?

Requiring only a function call to launch, our BodyScan is designed to be triggered from anywhere within your app.

Below is a typical journey a user will go through to access the scan and subsequently their scan results.

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The Scan Process

BodyScan Requirements
There are some important aspects that need to be considered prior to triggering the BodyScan. See Body Scan Requirements.





Step 1. Phone Alignment

Behind The Scenes
Image inspection utilizes our machine learning algorithms to analyze the images captured, ensuring there is a face, hands, and 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.

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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.

Step 2. Front Image Capture

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.

Step 2. Side Image Capture

Like the front capture, once the side profile is aligned, the process of capturing the side image profile will begin.

Step 3. Scan Success

If the scan is successful, the user will be altered of this on the following screen.

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Behind The Scenes
We process the images on the device using hardware-accelerated machine learning models. This means no images ever leave the device.



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