Sensor Analysis and Smart Health Platforms
CB2 Sensor Analysis and Smart Platforms division aims at providing health science researchers assistance with integrating wearable sensors and IoT devices into their study. Our primary goal is to help health science researchers bridge the gap between sensor integration and making data available for analysis and research.
Motivation of Service
Over the past few years, the use of consumer and medical grade sensors in health science projects has seen a massive increase. As an example in the consumer space there are over 31 million active Fitbit users, and over 100 million active Apple Watch users [source: Statista]. While these sensors can measure an impressive array of physiological markers such as Electrodermal Activity, Heart Rate Variability, Raw Blood Volume Pulse, Sleep stages, Gait, ECG and many more; each of these sensors has a non standard way of accessing the data. This often prevents researchers from effectively using these technologies in their studies.
Target Audience
How do I connect to the sensors I want to integrate?
How do I access the sensor data in real time?
Is what I am doing secure?
How do I use it for just-in-time interventions?
Where and how do I securely store the data?
Current/On-going Projects
- SensorFabric - A system of storing, querying, and analyzing heterogenous sensor data
- Low Code No Code (LCNC) mobile app development using MyDataHelps
- OB/GYN Gestational Weight gain project (MyDataHelps + Fitbit)
- Sensor Hub - Bluetooth Low Energy (BLE) custom sensor hub. Collaborator : Dr. Philipp Gutruf
- BioBAYB (Before and after your birth) - A study using the Oura ring of pregnant mothers for prediction postpartum mental health markers and labor onset. Collaborator : Dr. Elise Erickson
- Athletic Precision Training - A collaboration with McKale Memorial Center to study Fitbit metrics and correlate them with training metrics from in smart in gym sensors. A project funded by the UA SensorLab.
- CGM DPP Study - A collaboration with Nutritional Science, to integrate Fitbit sensors, mobile health application using MyDataHelps and a Continuous Glucose Monitor.
Past/Completed Projects
- ASTEC HRV analysis using Empatica E4. Collaborator : Dr. Janine Hinton
- Sensor Data Analysis for Green Road (Bodyguard 2, with HRV). Collaborator : Dr. Esther Sternberg
Poster Presentations (Inter-departmental Collaboration)
- An introduction to SensorFabric. (VIEW POSTER)
- UA Capstone – Antaris - An IoT based system for at-home health interventions (VIEW POSTER)
- UA CON ASTEC - Mixed-Reality-based Interactive Nursing Training System Integrated with Artifical Intelligence: Realistic Training, Objective Evaluation, and Cognitive Activity Analysis (VIEW POSTER)
We offer the following services at various stages of the study
Low Code No Code Application development service (LCNC).
CB2’s Sensor Analysis and Smart Platform division offers and supports in partnership with Care Evolution, a low code application development platform called MyDataHelps. The platform allows health science researchers to create HIPAA compliant custom applications using simple drag-and-drop tools.
We can assist you in creating custom mobile health applications that meet regulatory requirements in a matter of days to collect study data and connect with select consumer wearable devices.
What each service has to offer
No code integration with FitBit, Apple Watch, Google Fit, Apple Health Kit, EHR, Geographical and Weather data
HIPAA-compliant storage, Application has passed UArizona risk assessment and can be included in IRB and grant applications
Facilitate JITAI (Just in time adaptive interventions) with scheduled and manual push notifications
Create teams for managing survey coordinators, designers, and data analysts
Manage participant enrollment, gauge pre-study participant interest via waitlists, manage electronic consent and inclusion
Design custom surveys, import existing surveys from REDCap, create complicated conditional flow in surveys
Create custom screens (requires coding) to increate user engagement and retention, OR use existing built-in screens
Easily query the data collected and export it or set up automatic pipelines for analysis or triggers (via Sensor Fabric)
Sensor Analysis Examples
