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Biometric measures captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health

Biosnap

by Varun Shenoy

What is it about?

Biometric measures captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in blood glucose, blood pressure, and weight can accurately track diabetes control, cardiovascular health, and assist with medication management. Apple's Health app is a secure and connected repository for all your health data.

App Details

Version
1.0
Rating
(2)
Size
15Mb
Genre
Medical Health & Fitness
Last updated
June 13, 2017
Release date
June 13, 2017
More info

App Screenshots

App Store Description

Biometric measures captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in blood glucose, blood pressure, and weight can accurately track diabetes control, cardiovascular health, and assist with medication management. Apple's Health app is a secure and connected repository for all your health data.

Currently, patients record their results and date of assessment in a physical notebook. It may be months before a doctor can review a patient’s records. As smartphone use increases, health tracking platforms can be pivotal to providing the right care at the right time.

Biosnap enables you to capture medical monitor data with a picture and store it automatically in your Health app swiftly without the usage of the mobile keyboard. With Biosnap, there's no reason to buy an expensive internet-connected medical monitor or maintain a log of measured biomarkers. We turn non-connected medical monitors into smart monitors in a snap!

Features:

• Simple and clean interface with vibrant colors
• Native support for all iPhone devices running iOS 8.2 and above
• Full Health app support for blood pressure (systolic/diastolic), weight, and glucose levels
• Robust and accurate machine learning predictions for digit recognition on seven segment displays
• An active voice assistant throughout the application

Built by Varun Shenoy under the guidance of Prof. Oliver Aalami.

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