Wearable Technologies For High BMI

Mentor(s):

Masoud Sadjadi, Jessica Ramella-Roman Ph.D.

Product Owner(s):

Jessica Ramella-Roman Ph.D.

Description:


Obesity affects more than 20% of the US population, and disproportionately affects ethnic minorities. Affordable devices such as smartwatches and sensors can help users keep track of their fitness levels. Unfortunately, wearable technologies (e.g. heart rate sensors and smartwatches) do not often account for the physiological changes that characterize these individuals. For example, when getting the heart rate from an individual, the LED on the watch shines a light through the skin that reflects back, and the watch creates a graph of your pulse. However, for individuals with high bmi, their is a ticker layer of tissue that the light must traverse through, therefore the signal is weaker. As a result, measurements for blood pressure, heart rate, and other medical diagnostics in these individuals are often inaccurate. This project seeks to assist researchers by providing them with an app that facilitates and streamlines the data collection process. With sufficient data, computational models can be developed that account for the physiological changes present in these individuals and allow wearable technologies to take accurate measurements. This is the first version of the project in the form of an app for Android WearOS smartwatches. Although these watches often have proprietary apps that can provide certain physiological measurements such as heart rate, they often give inaccurate readings due to the way they process data from the device’s sensors. This app provides the reading directly from the device’s sensor in an effort to be as accurate as possible, additionally it possesses other features that streamline the process of gathering data. Moreover, on applicable smartwatches, PPG data will be able to be tracked as well. The data is saved in a .txt file, formatted so it can be easily be read in MATLAB by saving two attributes in two separate columns; timestamp and value of the sensor being tracked at that moment. The file is stored in Google Drive on the account that is logged in on the accompanying phone that it is paired with.

Team Members

David Reyes

Enzo Mendoza

Demo