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Movement recognition technology as a method of assessing spontaneous general movements in high risk infants
Claire Marcroft1, Aftab Khan2, Nicholas D Embleton3
1Neonatal Service, Royal Victoria Infirmary (RVI), Newcastle upon Tyne Hospitals NHS Foundation Trust , Newcastle upon Tyne , UK ; MoveLab, The Medical School, Newcastle University , Newcastle upon Tyne , UK.
Insights
Movement recognition technology aids early identification of neurological and motor impairments in preterm infants. This approach offers objective, quantitative assessment to improve therapeutic intervention timing for better outcomes.
Area of Science:
- Neurology
- Pediatrics
- Biomedical Engineering
Background:
- Preterm birth elevates risks for neurological and motor impairments, including cerebral palsy, particularly in extremely premature infants.
- Early identification of high-risk infants is crucial for timely therapeutic interventions, but current methods present challenges.
- Spontaneous general movements assessment is a key clinical tool for predicting motor impairments in at-risk infants.
Purpose of the Study:
- To identify recent translational studies utilizing movement recognition technology for assessing movement in high-risk infants.
- To explore the application of computerized approaches for continuous, objective, and quantitative analysis of infant limb movements.
- To highlight the potential of movement recognition in improving early detection and intervention for motor impairments.
Main Methods:
- Review of translational studies employing movement recognition technology in high-risk infant populations.
- Exploration of diverse recording methods, including camera-based systems and body-worn sensors.
- Application of machine learning algorithms for analyzing time-series movement data to detect and classify atypical movements.
Main Results:
- Movement recognition technologies, using sensors or cameras, enable continuous, objective, and quantitative assessment of infant movements.
- Machine learning effectively analyzes recorded movement data for detecting and classifying atypical spontaneous general movements.
- These technologies show promise in identifying infants at high risk for motor impairments.
Conclusions:
- Movement recognition technology represents a significant advancement in assessing movement in high-risk infants.
- This technology facilitates early identification of neurological and motor impairments, enabling timely interventions.
- Inter-disciplinary collaboration in applying movement recognition holds potential for understanding infant neurodevelopment and improving pediatric care.
Abstract:
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.
