eWEAR: Soft-Bioelectronics to Detect Neuromotor Disorders in Infants
Meeting Reports
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Apr 4, 2022
Summary
Researchers are exploring the use of soft, flexible, wireless bioelectronics to detect neuromotor disorders in infants, addressing the challenges of current bulky and wired systems. Traditional assessments, like Prechtel’s tool, predict cerebral palsy in infants by analyzing general movements (GMs), but these methods rely heavily on video monitoring and expert evaluation. A new approach, led by Prof. Zhenan Bao and others, involves a soft-electronic, wireless sensor network that captures real-time motion data from infants. This data, processed using artificial intelligence and machine learning, can detect atypical movements, revealing hidden signs of neuromotor issues early on. The low-cost and scalable technology promises to make neurological disorder detection more accessible, especially in remote settings.
Summary
Researchers are exploring the use of soft, flexible, wireless bioelectronics to detect neuromotor disorders in infants, addressing the challenges of current bulky and wired systems. Traditional assessments, like Prechtel’s tool, predict cerebral palsy in infants by analyzing general movements (GMs), but these methods rely heavily on video monitoring and expert evaluation. A new approach, led by Prof. Zhenan Bao and others, involves a soft-electronic, wireless sensor network that captures real-time motion data from infants. This data, processed using artificial intelligence and machine learning, can detect atypical movements, revealing hidden signs of neuromotor issues early on. The low-cost and scalable technology promises to make neurological disorder detection more accessible, especially in remote settings.
