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Movement Recognition Technology as a Method of Assessing Spontaneous General Movements in High Risk Infants
Open accessAbstract
Preterm birth is associated with increased risks of neurological and motor impairments such as cerebral palsy. The risks are highest in those born at the lowest gestations. Early identification of those most at risk is challenging meaning that a critical window of opportunity to improve outcomes through therapy-based interventions may be missed. Clinically, the assessment of spontaneous general movements is an important tool, which can be used for the prediction of movement impairments in high risk infants. Movement recognition aims to capture and analyze relevant limb movements through computerized approaches focusing on continuous, objective, and quantitative assessment. Different methods of recording and analyzing infant movements have recently been explored in high risk infants. These range from camera-based solutions to body-worn miniaturized movement sensors used to record continuous time-series data that represent the dynamics of limb movements. Various machine learning methods have been developed and applied to the analysis of the recorded movement data. This analysis has focused on the detection and classification of atypical spontaneous general movements. This article aims to identify recent translational studies using movement recognition technology as a method of assessing movement in high risk infants. The application of this technology within pediatric practice represents a growing area of inter-disciplinary collaboration, which may lead to a greater understanding of the development of the nervous system in infants at high risk of motor impairment.
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“As mentioned before, direct sensing schemes (e.g., inertial sensors, magnetic tracking devices) are not optimally suited for infants due to bulk, size, rigid construction, and/or wired connections (22).”
From Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants (2021) · cites this paper as [22]
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Metadata
- Authors
- Marcroft, C., Khan, A., Embleton, N. D., Trenell, M., & Plötz, T.
- Affiliations
- Royal Victoria Infirmary (GB)
- Newcastle University (GB)
- Corresponding author
- Marcroft, Claire
- Venue
- Frontiers in Neurology
- Publisher
- Frontiers Media SA
- Published
- 2015-01-09
- Volume
- 5
- Pages
- 284-284
- ISSN
- 1664-2295 (ISSN-L)
- PMC
- PMC4288331
- OpenAlex
- W2076572096
- Cited by
- 146
- References
- 73
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 11 · 2023: 15 · 2022: 18 · 2021: 13 · 2020: 18 · 2019: 19 · 2018: 9 · 2017: 12 · 2016: 10 · 2015: 8
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neurology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- National Institute for Health and Care Research (SRF-2011-04-017)
- Research Councils UK
- Medical Research Council (MR/K006312/1)
- Topics
- Health Sciences › Medicine › Rehabilitation › Cerebral Palsy and Movement Disorders
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Infant Development and Preterm Care
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Neonatal and fetal brain pathology
- Keywords
- motion recognition, high-risk infants, preterm infants, cerebral palsy, motion sensors, machine learning, early risk detection, infant motor development
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