flow chart for Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset paper

Wearables Can Detect The Flu? Well…Maybe…

Surprisingly there are no pre-symptomatic screening methods for the common cold or the flu, allowing these viruses to spread unbeknownst to the infected. However, if we could detect when infected people will get sick even before they were showing symptoms, we could do a lot more to contain the flu or common cold and possibly save lives. Well, that’s what this group of researchers in this highly collaborative study set out to accomplish using data from wearable devices.

Participants of the study were given an E4 wristband, a research-grade wearable that measures heart rate, skin temperature, electrodermal activity, and movement. They then wore the E4 before and after inoculation of either influenza or rhinovirus. The researchers used 25 binary, random forest classification models to predict whether or not participants were infected based on the physiological data reported by the E4 sensor. Their results are pretty lengthy, so I’ll only highlight a few major discussion points. In one particular analysis, they found that at 36 hours after inoculation their model had an accuracy of 89% with a 100% sensitivity and a 67% specificity. Those aren’t exactly world-shaking numbers, but something the researchers thought was pretty promising nonetheless.

One major consideration for the accuracy of their model is the quality of the data reported by the wearable. Namely, if the data reported by the wearable isn’t reliable itself, no model derived from such data can be trustworthy either. We’ve discussed those points here at Hackaday before. Another major consideration is the lack of a control group. You definitely need to know if the model is simply tagging everyone as “infected” (which specificity does give us an idea of, to be fair) and a control group of participants who have not been inoculated with either virus would be one possible way to answer that question. Fortunately, the researchers admit this limitation of their work and we hope they will remedy this in future studies.

Studies like this are becoming increasingly common and the ongoing pandemic has motivated these physiological monitoring studies even further. It seems like wearables are here to stay as the academic research involving these devices seems to intensify each day. We’d love to see what kind of data could be obtained by a community-developed device, as we’ve seen some pretty impressive DIY biosensor projects over the years.

E4 Empatica device for measuring location, temperature, skin conductance, sleep, etc. on arm

Choosing The Optimal Sampling Rate For Your DIY Heart Rate Monitor

With wearables still trying to solidify themselves in the consumer health space, there are a number of factors to consider to improve the reliability of such devices in monitoring biometrics. One of the most critical such parameters is the sampling rate. By careful selection of this figure, developers can minimize errors in the measurement, preserve power, and reduce costs spent on data storage. For this reason, [Brinnae Bent] and [Dr. Jessilyn Dunn] wanted to determine the optimal sampling rate for wrist-worn optical heart rate monitors. We’ve shared their earlier paper on analyzing the accuracy of consumer health devices, so they’ve done a lot of work in this space.

The results of their paper probably don’t surprise anyone. The lower the sampling rate, the lower the accuracy of the measurement, and the higher the sampling rate the more accurate the measurement when compared to the gold standard electrocardiogram. They also found that metrics such as root mean square of successive differences (RMSSD), used for calculating heart rate variability, requires sampling rates greater than 64 Hz, the nominal sampling rate of the wearable they were investigating and of other similar devices. That might suggest why your wearable is a bit iffy when monitoring your sleeping habits. They even released the source code for their heart rate variability analysis, so there’s a nice afternoon read if you were looking for one.

What really stood out to us about their work is how they thoroughly backed up their claims with data. Something crowdfunding campaigns could really learn from.