Title: Development of the Baby SmaryPants: Robotic System for the Analysis of Infant Motor Development 

Katelyn Elizabeth Fry-Hilderbrand

Robotics Ph.D. Candidate

Georgia Institute of Technology

Email: katelyn.fry@gatech.edu

Date: Friday, April 15, 2022

Time: 10:30 AM to 12:30 PM (EST)

Meeting Link: https://gatech.zoom.us/j/91419066323?pwd=eE9kbGRRUlUwWmhvRTMrQ0h2aEZLUT09

Committee:

Dr. Ayanna Howard (Advisor) -- School of Engineering, The Ohio State University

Dr. Yu-Ping Chen -- Department of Physical Therapy, Georgia State University

Dr. Patricio Vela -- School of Electrical and Computer Engineering, Georgia Institute of Technology

Dr. Jun Ueda -- School of Mechanical Engineering, Georgia Institute of Technology

Dr. Eva Dyer -- School of Biomedical Engineering, Georgia Institute of Technology

Abstract:

One of the earliest displays of motor skills in infants is spontaneous kicking behaviors. Any abnormalities in this kicking behavior or delays in typical motor development are important indicators of neurodevelopmental abnormalities. Additionally, the early detection of delays and abnormalities is vital so to enable early intervention therapy to mitigate symptoms and improve overall quality of life. However, to date these abnormalities are not well defined and are thus extremely difficult to detect outside of direct clinical observation. Even in cases where clinical observations are possible, they are time consuming, expensive, and subject to clinician opinion and infant cooperation.

To address these limitations, we have developed an infant suit system (Baby SmartyPants system) to gather acceleration and angular rate data in the home setting and allow for the non-clinical observations of spontaneous kicking over an extended period of time. To date, the SmartyPants system has been used to gather data from 23 infants 1-8 months of age, 8 of whom were born premature. Based on the collection of measurement data from these 15 term, typically developing infants, we have developed a kinematic library of kicking features to describe typical kicking behaviors for infants across various ages. It is important to identify and codify normal kicking behaviors at various ages to provide a benchmark for comparison of a infant subject’s demonstrated kicking behaviors. Results suggest that certain features, such as kicking frequency and kicking duration, significantly correlate with age.

These significant features have been used in the development and validation of a model to provide a comprehensive estimate an infant’s developmental age from their kicking behavior. An infant would be considered developmentally delayed by this model if their kicking behavior is indicative of a younger infant. Additionally, this model has been used in the evaluation of kicking data gathered from 8 low-risk preterm infants. Estimations of age from this model (and from the comparison of individual features to normative trends) suggest that preterm infants display more mature kicking early in life, but mature at a slower rate than their term counterparts.