Researchers at Penn State have tested the use of wearable sensors and a machine learning algorithm to automatically monitor and evaluate general movements in infants. The technology can identify infants at risk of developing neuromotor diseases with up to 99.9% accuracy.
Apr 13 2024 Penn State A team of researchers led by Huanyu "Larry" Cheng, the James L. Henderson, Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State , tested the use of wearable sensors paired with a "tiny" machine learning algorithm to automatically monitor and evaluate general movements in infants.
Cheng: General movements are the innate, spontaneous movement patterns exhibited by infants from birth through 20 weeks. Atypical patterns of infant movement behaviors can indicate underlying neuromotor dysfunctions such as cerebral palsy, autism spectrum disorder or minor forms of other neurological disorders. Detection at the earliest time in infancy is critically important to promote early recovery and optimal long-term functional outcomes and quality of life.
Wearable Sensors Machine Learning Infants Neuromotor Diseases General Movements Penn State
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