Webinar: Manage and Analyze Wearable Data for Your Research Using CyVerse (a 2-part webinar)

Register for Part 2

Webinar materials (Part 1)

Slide presentation 

Videorecording 

About the webinar

Wearable fitness trackers such as Apple Watch, Fitbit, Garmin, and others are widespread and offer a scalable mechanism to collect physiological biomarkers such as heart rate, blood pressure, activity, sleep, etc. However, integrating this valuable data into your research projects can be challenging, with each sensor exposing an unique way for data ingestion and connection.

In this 2-part webinar Shravan Aras, Asst. Director Sensor Analysis & Smart Platforms at the University of Arizona, will go over some of the commonly used wearable sensors and present practical information on how to connect to their outputs and how to analyze wearable data for your research. This webinar will be particularly useful to biomedical researchers and healthcare professionals, but also data analysts and engineers interested in ML and AI applications.

For part 1 of the webinar (Sep 29), we will look at the various biomarkers recorded by some of the most popular wearable sensors (Fitbit, Apple Watch, Garmin, etc.) on the market. Dr. Aras will walkthrough how data from these sensors can easily be integrated into your research studies using MyDataHelps and CyVerse.

For part 2 (Oct 20), we will learn how to monitor participant compliance and explore how to visualize the sensor data running on CyVerse using Apache Superset, an open-source software application for data exploration and data visualization able to handle data at petabyte scale.

What you'll learn

Part 1: Integrate and Manage Wearables Data (September 29, 2023)

• How to connect to sensors you want to integrate in your research
• How to access sensor data in real time
• Where and how to securely store all the data

Part 2: Data Analysis & Visualization (October 20, 2023)

• how to visualize sensor data 
• how to monitor participant compliance
• how to access and use the Apache Superset to explore and visualize petabyte-scale data

About the presenter

Shravan Aras, PhD, Assistant Director, Sensor Analysis & Smart Health Platforms Shravan Aras helps integrate wearable sensor technology and smart health platforms such as IoT devices into various clinical studies run across UAHS centers. He graduated from the Department of Computer Science at University of Arizona in 2018. Dr. Aras' research areas span across energy optimization for sensors, clinical imaging using machine learning techniques, graded authentication based on biometrics and biomedical algorithms for cardiovascular systems. He is also instrumental in pushing the Low Code - No Code initiative, giving clinicians, and non-technical personnel the tools to develop sensor-based applications for distributed clinical data collection. His motto is to reduce the initial inertia for adopting sensor based mobile technologies in distributed at-home clinical studies, thus giving physicians, residents, and research personnel the tools needed to focus on their goals rather than the underlying computational technology.