The Internet of Humans

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The Internet of Humans
Sensor technology drawn from animal research could be used to track human h
Biodegradable sensors promise better injury rehab

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Unexpected findings from sensors implanted in animals, a practice known as biologging, should cause a seismic shift in how wearable sensors are used to promote health in humans.
That’s the conclusion of an international team of scientists from the Massachusetts Institute of Technology, US, King Abdullah University of Science and Technology, Saudi Arabia, and others from Spain and Australia, in a perspective piece published in the journal ACS Sensors.
The group says animals have been fitted with sensors to measure just one or two behaviours, yet scientists have gained dramatic and unforeseen insights into a wealth of other habits.
A case in point is the jaw sensor.
To the uninitiated this tiny device, which has been implanted in penguins, sea lions and dolphins, seems only able to know if the jaw is moving up or down.
But the scientists argue it is also of critical importance for the emerging science of wearable health monitoring, not least because most of us are already sensor-enabled.
“[R]oughly three billion people owned smartphones in 2018, effectively already being tagged with a subset of sensors,” Michael Lee and coauthors write.
Researchers have used smartphone tapping behaviour to predict Parkinson’s disease, analysed text and email data to detect depressed speech and even used the camera to measure heart rate by look at skin colour changes in the finger.
The idea is that just about everything going on in the human body will be picked up in part by a sensor, in the same way that a sea lion’s prey is “partially encoded” by its jaw movements.
“To what extent does your daily activity, the rate and quantity of steps that you take, ‘partially encode’ markers of your health like blood sugar or cancer risk?” the authors ask.
Likewise, to join the health dots in humans, scientists are going to need to start combining disparate, or what the authors call “orthogonal” data sets – ones where links aren’t initially suspected

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