A new wearable sensor tracks children’s movements and uses their body language to
Anxiety and depression plague millions of people in the US alone, and children are no exception.
Over seven percent of children in the US are estimated to have anxiety and some three percent struggle with depression.
But these estimates are likely low, as symptoms in children are different from those in adults and can be more difficult to detect.
Scientists at the University of Vermont are working on novel ways to diagnose children’s unique symptoms, including a new sensor and algorithm that translate the ways kids move into a clearer picture of their moods.
We know that adult diagnosis rates for disorders like anxiety and depression are low in part because we simply don’t want to talk about these feelings.
But young children don’t have the same language to do so – even if they wanted to.
So their signs of depression and anxiety come out in less obvious ways, and are sometimes mistaken for another illness or learning disability.
The proper therapies to treat each of these predicaments are very different from one another, so getting a correct diagnosis as quickly as possible is crucial to ensuring a child has a happy healthy life in front of them.
To do this, psychologists look to behavioral cues, but even then they’re getting a subjective view of one child at a time – not a comparison with what would be considered more typical actions.
So University of Vermont researchers created an algorithm to quantify which types of movements – tracked and logged by a wearable sensor – children make when they’re suffering anxiety or depression.
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