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 is said to have estimated core body temperatures with accuracy comparable to some consumer thermometers. The team has published its findings in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
“In undergrad, I was doing research in a lab where we wanted to show that you could use the temperature sensor in a smartphone to measure air temperature,” lead author Joseph Breda, a UW doctoral student in the Paul G Allen School of Computer Science & Engineering said in a statement. “When I came to the UW, my adviser and I wondered how we could apply a similar technique for health. We decided to measure fever in an accessible way. The primary concern with temperature isn’t that it’s a difficult signal to measure; it’s just that people don’t have thermometers.”
The app is the first to use existing phone sensors and screens to estimate whether people have fevers. Breda said it needs more training data to be widely used, but doctors can see the potential of such technology.
“People come to the ER all the time saying, 'I think I was running a fever.' And that’s very different than saying 'I was running a fever,'” said Dr Mastafa Springston, a co-author on the study and a UW clinical instructor at the Department of Emergency Medicine in the UW School of Medicine. “If people were to share fever results with public health agencies through the app, similar to how we signed up for COVID exposure warnings, this earlier sign could help us intervene much sooner.”
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Clinical-grade thermometers use thermistors to estimate body temperature. Off-the-shelf smartphones contain thermistors that are mostly used to monitor the temperature of the battery. The UW researchers realised they could use these sensors to track heat transfer between a person and a phone. The phone touchscreen could sense skin-to-phone contact, and the thermistors could gauge the air temperature and the rise in heat when the phone touched a body.
Testing
To test this idea, the team started by gathering data in a lab. To simulate a warm forehead, the researchers heated a plastic bag of water with a sous-vide machine and pressed phone screens against the bag. To account for variations in circumstances, such as different people using different phones, the researchers tested three phone models. They also added accessories such as a screen protector and a case and changed the pressure on the phone.
The researchers used the data from different test cases to train a machine learning model that used the complex interactions to estimate body temperature. Since the sensors are supposed to gauge the phone’s battery heat, the app tracks how quickly the phone heats up and then uses the touchscreen data to account for how much of that comes from a person touching it. As they added more test cases, the researchers were able to calibrate the model to account for the variations in things such as phone accessories.
The researchers then 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 with forefingers and thumbs touching the corner edges to reduce heat from the hands being sensed. Participants then pressed the touchscreen against their foreheads for about 90 seconds.
FeverPhone estimated patient core body temperatures with an average error of about 0.41oF (0.23oC), which is in the clinically acceptable range of 0.5oC.
The study didn’t include participants with severe fevers above 101.5oF (38.6oC), because these temperatures are easy to diagnose and because sweaty skin tends to confound other skin-contact thermometers, the team said. Also, FeverPhone was tested on three phone models. Training it to run on other smartphones, as well as devices such as smartwatches, would increase its potential for public health applications, the team said.
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