High tech wearable sensor can detect hidden anxiety and depression in young children much faster than traditional methods
- About three percent of children over three have depression and about seven percent struggle with anxiety in the US
- But children’s symptoms can be hard to detect
- Psychologists use behavioral observations that may take many sessions
- University of Vermont researchers have developed a wearable motion sensor that tracks kids movements and analyzes their meaning with an algorithm
- The new technology can do in seconds what might take doctors hours
A new wearable sensor tracks children’s movements and uses their body language to distinguish between children with or without anxiety or depression.
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.
Children’s anxiety and depression sometimes manifest in difficult to detect ways, but a new sensor and algorithm developed by the University of Vermont can do it in seconds
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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The researchers put the kids – some of whom had anxiety or depression, all kitted out with their sensors – in situations that were meant to arouse some kind of mood and behavioral response in them.
For example, to see how differently anxious children would act in an anxiety-provoking situation, they took kids into a dimly-lit room, and hinted at a dangerous sleeping creature in a covered terrarium.
Then, they sprung the actual fake snake on the kids suddenly, tracking their movements all along.
No children were harmed in the making of this experiment, which the researchers made sure of after the fact by reassuring the kids and letting them play with the snake.
The crucial moment was the one of anticipation, while the children were waiting to find out what it was they needed to keep quite around. Sensors picked up head, body and limb movements, and the algorithm interpreted them.
‘The way that kids with internalizing disorders’ – such as anxiety and depression – ‘moved was different than those without,’ says Ryan McGinnis, a biomedical engineer and co-author of the study.
Kids with internalizing disorders tend to turn away from the unknown they are anticipating, for example.
Typically, to identify these behaviors, children would have to do multiple such tests and doctors would have to watch and re-watch videos to identify the subtle distinctions.
‘Something that we usually do with weeks of training and months of coding can be done in a few minutes of processing with these instruments,’ said another co-author, psychologist Ellen McGinnis.
The algorithm managed to pick out the children with mood disorders from the ones without them in a matter of 20 seconds of sensor data.
‘Children with anxiety disorders need an increased level of psychological care and intervention. Our paper suggests that this instrumented mood induction task can help us identify those kids and get them to the services they need,’ said Ellen McGinnis.
The faster a child is diagnosed, the earlier they can get treatment, and the better their chances of avoiding a lifelong struggle with mood orders will be.
The team soon hopes to add additional assessments that use data and can parse different disorders out from one another, so that clear, quick tests for kids’ mental health could be incorporated into schools and doctors offices.
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