Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants

Details

• Personal Author:
  Bao B ; Cheng H ; Cheng X ; Cui W ; Guo K ; Li H ; Lin Y ; Liu S ; Tong Y ; Xu H ; Yang H ; Yang K ; Zhang S ; Zhang Y ; Zhu J
• Description:
  General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants' general movements can be captured digitally, but the lack of quantitative assessment and well-trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low-resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy-to-use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full-body motion data. The proof-of-the-concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence-based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders. [Description provided by NIOSH]
• Subjects:
  Infant, Newborn Occupational Health Safety
• Keywords:
  Brain Function Developmental Disorders Machine Learning Motion Studies Neonates Sensors Wearable Technologies
• ISSN:
  2198-3844
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  11
• Issue:
  19
• NIOSHTIC Number:
  nn:20069628
• Citation:
  Adv Sci 2024 May; 11(19):2306025
• Contact Point Address:
  Huanyu Cheng, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA, 16802 USA
• Email:
  huanyu.cheng@psu.edu
• Federal Fiscal Year:
  2024
• Performing Organization:
  Pennsylvania State University
• Peer Reviewed:
  True
• Start Date:
  20220901
• Source Full Name:
  Advanced Science
• End Date:
  20240831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:fb4fb7a8fcee36dbd5864489b6cef9154b0481023f655327714c4746a9cafff866a6cb1627f9d5eba3e84bfe58c0331d0d0892f352eff948dc7042cc5c39695a
• Download URL:
  https://stacks.cdc.gov/view/cdc/208018/cdc_208018_DS1.pdf
• File Type:
  [PDF - 4.72 MB ]