Study
IDATA
(Learn more about this study)
Project ID
IDATA-33
Initial CDAS Request Approval
Jan 6, 2020
Title
Integration and Analysis of Wearable Sensor Data for Biomarker Discovery
Summary
The continuous measurement of vital signs, physical activity, and biochemistry facilitates detection and intervention of acute events such as ischemic stroke or heart failure and assists in monitoring disease and treatment progression. However, accuracy, validity, and reliability of consumer-grade wearable devices and the algorithms used to derive insights about the well-being of patients need to be established and improved. This in turn creates the need for methods to perform data fusion to obtain meaningful information from multiple sensor data streams. More challenging is the integration of sensor data with clinical, omics, and research data that provide biological context to the sensor information. We propose to develop GT-WSI an innovative platform addressing the challenges of integrating wearable sensor data with patient health information including clinical, omics, demography, and drug treatment regimens.
Aims
1. Assemble data and design data models to automate the integration of clinical and sensor data, to represent and integrate the diverse formats used to transport the study, sensor, and clinical data needed for epidemiological studies, and to harmonize this data to domain knowledge represented in ontologies.
2. Develop bioinformatics methods for wearable sensor data processing and quality control approaches for the evaluation of quality from wearable sensor data, including time-series alignment, outlier detection, error handling, and biosensor integration.
3. Evaluate the methods in real-world biosensor integration and analysis workflows and validate the platform against published studies and demonstrate the ability to identify digital biomarkers within the data.
Collaborators
Mansur R. Kabuka, Ph.D., INFOTECH Soft
Ray M. Bradley, INFOTECH Soft
