Method for data acquisition and assessments of neuromotor control development using brief video recordings.
Diagnosis of neurodevelopmental disorders relies on clinical tests that depend on observation of a set of behavioral benchmarks that are then mapped to a scale. These scales are not standardized for anatomical differences and nonlinear rates of growth in early life. They rely on subjective observation of patients and consequently, are prone to miss other key patterns and signs that are difficult to identify with the naked eye. There is a large need for more comprehensive methods integrating rapid physical growth and motor control, to evaluate proper neurodevelopment of neonates, leading to very early diagnosis of neurodevelopmental derailment.
Rutgers researchers have developed a strategy for building a dynamic chart for mapping and predicting the neurodevelopment of babies. The team combined physical growth with motor motion analysis and auditory brain stem response (ABR) to identify signature patterns that can be attributed to the first three months of life of a child’s development. By quantitatively determining the average neurodevelopmental pattern of children, this technology holds the promise of early diagnosis of disorders or deviations that could be addressed early in a child’s growth. Moreover, because this technology has validated commercially available with research-grade motion caption, it is scalable for both public and clinical uses.
- Mobile app for parents and pediatricians for child neurodevelopment assessment at scale
- Diagnosis of neurodevelopmental disorders
- Non-intrusive and quick diagnostic approach for neurodevelopment assessment
- Combination of motion and ABR data
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1. Torres, E. B., Smith, B., Mistry, S., Brincker, M. & Whyatt, C. Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control. Front Pediatr 4, 121, doi:10.3389/fped.2016.00121 (2016).
2. Rai, R. New Techniques to Facilitate Longitudinal Video-Based Digital Tracking of Infant Development Master thesis, Rutgers, The State University of New Jersey, Graduate Studies, (2021) under Torres supervision.