A team led by researchers at the University of Washington has created an app called FeverPhone, which transforms smartphones into thermometers without adding new hardware. Instead, it uses the phone's touchscreen and repurposes the existing battery temperature sensors to gather data that a machine learning model uses to estimate people’s core body temperatures.
When the researchers tested FeverPhone on 37 patients in an emergency department, the app estimated core body temperatures with accuracy comparable to some consumer thermometers. The team published its findings in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
The researchers took FeverPhone to the UW School of Medicine’s Emergency Department for a clinical trial where they compared its temperature estimates against an oral thermometer reading. They recruited 37 participants, 16 of whom had at least a mild fever.
To use FeverPhone, the participants held the phones like point-and-shoot cameras — with forefingers and thumbs touching the corner edges to reduce heat from the hands being sensed (some had the researcher hold the phone for them). Then participants pressed the touchscreen against their foreheads for about 90 seconds, which the researchers found to be the ideal time to sense body heat transferring to the phone. Overall, FeverPhone estimated patient core body temperatures with an average error of about 0.41 degrees Fahrenheit (0.23 degrees Celsius), which is in the clinically acceptable range of 0.5 C.
Shwetak Patel, a UW professor in the Allen School and the electrical and computer engineering department, was a senior author on the paper, and Alex Mariakakis, an assistant professor in the University of Toronto’s computer science department, was a co-author.
