Elena Kokkoni

Feasibility and Effectiveness of a Novel Exoskeleton for an Infant With Arm Movement Impairments.

Purpose: To determine whether a novel exoskeletal device (Pediatric-Wilmington Robotic Exoskeleton [P-WREX]) is feasible and effective for intervention to improve reaching and object interaction for an infant with arm movement impairments. Methods: An 8-month old infant with arthrogryposis was followed up every 2 weeks during a 1-month baseline, 3-month intervention, and 1-month postintervention. At each visit, reaching and looking behaviors were assessed. Results: Within sessions, the infant spent more time contacting objects across a larger space, contacting objects with both hands, and looking at objects when wearing the P-WREX. Throughout intervention, the infant increased time contacting objects both with and without the device and increased bilateral active shoulder flexion. Conclusions: (1) It may be feasible for families to use exoskeletons for daily intervention, (2) exoskeletons facilitate immediate improvements in function for infants with impaired upper extremity mobility, and (3) interventions using exoskeletons can improve independent upper extremity function across time.

Learning models of human-robot interaction from small data

This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets.

Infant sitting postural control appears robust across changes in surface context

Abstract Aim of the study: Independent sitting requires the control of the involved body segments over the base of support using information obtained from the three sensory systems (visual, vestibular, and somatosensory). The contribution of somatosensory information in infant sitting has not been explored. To address this gap, we altered the context of the sitting support surface and examined the infants’ immediate postural responses. Materials and methods: Ten 7-month-old typically developing infants sat on compliant and firm surfaces in one session. Spatial, frequency, and temporal measures of postural control were obtained using center of pressure data. Results Our results suggest that infants’ postural sway is not immediately affected by the different types of foam surface while sitting. Conclusions: It seems that mature sitter infants are able to adapt to different environmental constraints by disregarding the distorted somatosensory information from the support surface and relying more on their remaining senses (visual and vestibular) to control their sitting posture.