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WASHINGTON, Jan. 17 (BelTA – Xinhua) – Children are not as happy as expected. About 20% of them suffer from anxiety and depression, but these internalized disorders are difficult to detect.
A study published Wednesday in the journal PLOS ONE described a tool to detect children for their internalization of disorders early and accurately.
Researchers from the University of Vermont and the University of Michigan have badociated a sensor and algorithm with a method that elicits children's behaviors and feelings, such as anxiety.
Children were asked from a dimly lit room to anticipate something to look into a covered glbad box, which turned out to be a fake snake. Then the researchers noted their responses, traditionally through recorded video, but in the new study, aromatically by a portable motion sensor and the machine learning algorithm.
The new tool identified the differences between children with internalized disorders and others. According to this study, accuracy reaches 81%, which is higher than the standard questionnaire for parents.
The algorithm learned that the movement of children before the snake was revealed was the most indicative factor, as those with internalizing disorders tended to shy away from the potential threat.
This showed that they anticipated more anxiety and that the rejection behavior was a negative reaction.
It takes only 20 seconds of data from the anticipation phase provided by the sensors and the algorithm to make a decision, while the traditional video coding method can take several months.
This opens up opportunities for large scale screening to identify depressed anxious children.
Early intervention is essential because the brains of young children are extremely malleable and respond well to treatment, said co-author of the article, Maria Muzik, of the University of Michigan.
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