New technology can automatically identify infants at risk of developing neuromotor diseases

A team of researchers led by Huanyu “Larry” Cheng at Penn State developed wearable sensors and a machine learning algorithm to monitor infant movements for early detection of neuromotor diseases. The pilot study showed up to 99.9% accuracy in identifying at-risk infants. This technology is needed to detect issues early, before irreversible damage occurs. The sensors are designed to be soft and placed strategically on the infant’s body, with data processed by a tiny machine-learning algorithm. This method is faster and more efficient compared to traditional examination methods. Future research aims to validate the system with a larger study and explore other applications beyond infant monitoring.

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