Smartwatch-Based Algorithm Detects Early Signs of Viral Infections, Including COVID-19

Purdue University (West Lafayette, IN, USA) and physIQ (Chicago, IL, USA) have announced the co-development of a viral detection algorithm for smartwatches. This innovation will be the result of a collaboration between physIQ and university engineers. The algorithm will be commercialized by physIQ, which develops solutions designed to improve health care outcomes by applying artificial intelligence (AI) to real-time physiological data from wearable sensors.

The research involved a study of 100 participants, including Purdue students, staff and faculty, to determine whether wearing a smartwatch to collect data was practical, unobtrusive and user-friendly. Each participant received a Samsung Galaxy smartwatch with a pre-loaded physIQ app to collect data. Along with the smartwatch, they also wore FDA-cleared adhesive chest-based biosensors to capture a single-lead electrocardiogram signal and multiple other parameters for five days of continuous monitoring. The researchers then analyzed data from the app remotely using physIQ's cloud-based accelerateIQ platform.

Data from the chest patches were processed by physIQ's U.S. Food and Drug Administration-cleared AI-based algorithms in deriving heart rate, respiration rate and heart rate variability. These data served as "gold standard" references to compare with data from the smartwatches. The viral infection detection algorithm complements physIQ's other health care applications. The goal across all of physIQ's applications is the ability to characterize dynamic human physiology over time, whether it is for assessing the efficacy of a new therapy, safety monitoring during treatment or general wellness.

"Smartwatches are well-suited for the detection of early viral infection, including COVID-19," said Craig Goergen, Purdue's Leslie A. Geddes Associate Professor of Biomedical Engineering, who led the research. “Infections can happen at any time, making the continuously tracked data available through an individual's smartwatches uniquely suited to identify the earliest signs of illness. In particular, knowledge of a person's usual heart rate and respiratory during sleep and activity over long periods of time is especially valuable for detecting subtle changes from normal.”

"The algorithms for enabling early detection are built off physiological features derived from the biosensor data collected by the smartwatches," said Stephan Wegerich, physIQ's chief science officer. "Generating accurate and robust physiological features forms the input to subsequent viral detection algorithms. This requires the development of sophisticated signal processing and machine learning algorithms. Combined, these make the most out of smartwatch biosensor data, which is a big part of our collaboration with Purdue."

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