Modeling gross motor developmental curves of extremely and very preterm infants using the AIMS home-video method

BACKGROUND: Motor development is one of the first signals to identify whether an infant is developing well. For very preterm (VPT) infants without severe perinatal complications, little is known about their motor developmental curves. AIMS: Explore gross motor developmental curves from 3 until 18 months corrected age (CA) of VPT infants, and related factors. Explore whether separate profiles can be distinguished and compare these to profiles of Dutch term-born infants. STUDY DESIGN: Prospective cohort study with parents repeatedly recording their infant, using the Alberta Infant Motor Scale (AIMS) home-video method, from 3 to 18 months CA. SUBJECTS: Forty-two Dutch infants born ≤32.0 weeks gestational age and/or with a birthweight (BW) of <1500 g without severe perinatal complications. OUTCOME MEASURES: Gross motor development measured with the AIMS. RESULTS: In total 208 assessments were analyzed, with 27 infants ≥five assessments, 12 with <four, and three with one assessment. Sigmoid-shaped gross motor curves show unidirectional growth and variability. No infant or parental factors significantly influenced motor development, although a trend was seen for the model where lower BW, five-minute Apgar score <7, and Dutch native-speaking parents were associated with slower motor development. Three motor developmental profiles of VPT infants were identified, early developers, gradual developers, and late bloomers, which until 12 months CA are comparable in shape and speed to profiles of Dutch term-born infants. CONCLUSIONS: VPT infants show great intra- and interindividual variability in gross motor development, with three motor profiles being distinguished. From 12 months CA onwards, VPT infants appear to develop at a slower pace. With some caution, classifying infants into motor developmental profiles may assist clinical decision-making

Adaptation of the difficulty level in an infant-robot movement contingency study

19th International Workshop of Physical Agents (WAF). Madrid (22-23 Noviembre 2018)ABSTRACT: This paper presents a personalized contingency feedback adaptation system that aims to encourage infants aged 6 to 8 months to gradually increase the peak acceleration of their leg movements. The ultimate challenge is to determine if a socially assistive humanoid robot can guide infant learning using contingent rewards, where the reward threshold is personalized for each infant using a reinforcement learning algorithm. The model learned from the data captured by wearable inertial sensors measuring infant leg movement accelerations in an earlier study. Each infant generated a unique model that determined the behavior of the robot. The presented results were obtained from the distributions of the participants' acceleration peaks and demonstrate that the resulting model is sensitive to the degree of differentiation among the participants; each participant (infant) should have his/her own learned policy.This work was supported by NSF award 1706964 (PI: Smith, Co-PI: Matarić). In addition, this work was developed during an international mobility program at the University of Southern California being also partially funded by the European Union ECHORD++ project (FP7-ICT-601116), the LifeBots project (TIN2015-65686-C5) and THERAPIST project (TIN2012-38079)