4108 Artificial Intelligence-Based Quantification of the General Movement Assessment Using Center of Pressure Patterns in Healthy Infants Article Swipe
Tara Johnson
,
Namarta Kapil
,
Alexa Celeste Escapita
,
Bittu Majmudar
,
Safeer F. Siddicky
,
Junsig Wang
,
Erin M. Mannen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1017/cts.2020.324
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1017/cts.2020.324
OBJECTIVES/GOALS: One in six children in the U.S. has a Neurodevelopmental Disability (NDD). Prechtl’s General Movement Assessment (GMA) is a qualitative predictor of early motor dysfunction. However, no quantitative biomechanical assessment exists to more accurately identify all patients with NDD. METHODS/STUDY POPULATION: With UAMS IRB approval, as part of a larger study, healthy infants were filmed while lying supine on a force plate for 2 minutes. We studied 12 healthy full-term infants (gestational age: 38.9±1.5 weeks, age: 2.1-7.0 months; 7M, 5F; length: 64.0±5.2 cm; weight: 7.2±1.3 kg). Within our data set there were 3 infants transitioning to fidgety period (≤3 months), 4 in the fidgety period, (3-5 months), and 5 that matured beyond fidgety period (>5 months). Center of pressure (COP) path-lengths were gathered from the force plate at 1000 Hz. We grouped our data with K-means clustering and performed statistical analysis with ANOVA. RESULTS/ANTICIPATED RESULTS: We divided our data into 3 distinct clusters. The first group contained infants with moderate variability of movements which included 2 infants between 3 and 5 months and 2 infants slightly outside of this range. The second group, with mild variability in movements, included 4 infants between 2 and 3 months as well as 2 infants just older than 5 months. The third group, with little variability in movements, included 2 infants older than 5 months. A GMA reader (TJ) qualitatively confirmed these findings with video footage. Using a threshold of p<0.05, data sets within the clusters were similar and significantly different from other clusters. DISCUSSION/SIGNIFICANCE OF IMPACT: Fidgety infants have greater variability in COP patterns than their mature counterparts. We anticipate additional COP measurements will correspond with qualitative GMA analyses. Artificial Intelligence-based quantification of the GMA may be useful in earlier detection or prediction of NDD outcomes.
Related Topics
Concepts
Movement assessment
Supine position
Population
Gestational age
Center of pressure (fluid mechanics)
Birth weight
Psychology
Medicine
Pediatrics
Physical medicine and rehabilitation
Motor skill
Developmental psychology
Surgery
Pregnancy
Biology
Genetics
Aerospace engineering
Aerodynamics
Engineering
Environmental health
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1017/cts.2020.324
- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DDB12E3D68A34CB6EF116CD7B56105A5/S2059866120003246a.pdf/div-class-title-4108-artificial-intelligence-based-quantification-of-the-general-movement-assessment-using-center-of-pressure-patterns-in-healthy-infants-div.pdf
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3045947207
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3045947207Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1017/cts.2020.324Digital Object Identifier
- Title
-
4108 Artificial Intelligence-Based Quantification of the General Movement Assessment Using Center of Pressure Patterns in Healthy InfantsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-01Full publication date if available
- Authors
-
Tara Johnson, Namarta Kapil, Alexa Celeste Escapita, Bittu Majmudar, Safeer F. Siddicky, Junsig Wang, Erin M. MannenList of authors in order
- Landing page
-
https://doi.org/10.1017/cts.2020.324Publisher landing page
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https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DDB12E3D68A34CB6EF116CD7B56105A5/S2059866120003246a.pdf/div-class-title-4108-artificial-intelligence-based-quantification-of-the-general-movement-assessment-using-center-of-pressure-patterns-in-healthy-infants-div.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DDB12E3D68A34CB6EF116CD7B56105A5/S2059866120003246a.pdf/div-class-title-4108-artificial-intelligence-based-quantification-of-the-general-movement-assessment-using-center-of-pressure-patterns-in-healthy-infants-div.pdfDirect OA link when available
- Concepts
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Movement assessment, Supine position, Population, Gestational age, Center of pressure (fluid mechanics), Birth weight, Psychology, Medicine, Pediatrics, Physical medicine and rehabilitation, Motor skill, Developmental psychology, Surgery, Pregnancy, Biology, Genetics, Aerospace engineering, Aerodynamics, Engineering, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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