Infants born preterm have impaired abilities to interact with objects even in the first months of life. This impairment likely limits the knowledge they acquire about objects and about how they can act on them; this limited knowledge may, in turn, impair their early learning abilities. These results highlight the need for assessment and intervention tools specific for object exploration in young infants.

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 with arthrogryposis was followed every two weeks during a 1-month baseline, 3-month intervention, and 1-month post-intervention. 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.

Background: There is a lack of early (infant) mobility rehabilitation approaches that incorporate natural and complex environments and have the potential to concurrently advance motor, cognitive, and social development. The Grounded Early Adaptive Rehabilitation (GEAR) system is a pediatric learning environment designed to provide motor interventions that are grounded in social theory and can be applied in early life. Within a perceptively complex and behaviorally natural setting, GEAR utilizes novel body-weight support technology and socially-assistive robots to both ease and encourage mobility in young children through play-based, child-robot interaction. This methodology article reports on the development and integration of the different system components and presents preliminary evidence on the feasibility of the system.Methods: GEAR consists of the physical and cyber components. The physical component includes the playground equipment to enrich the environment, an open-area body weight support (BWS) device to assist children by partially counter-acting gravity, two mobile robots to engage children into motor activity through social interaction, and a synchronized camera network to monitor the sessions. The cyber component consists of the interface to collect human movement and video data, the algorithms to identify the children's actions from the video stream, and the behavioral models for the child-robot interaction that suggest the most appropriate robot action in support of given motor training goals for the child. The feasibility of both components was assessed via preliminary testing. Three very young children (with and without Down syndrome) used the system in eight sessions within a 4-week period.Results: All subjects completed the 8-session protocol, participated in all tasks involving the selected objects of the enriched environment, used the BWS device and interacted with the robots in all eight sessions. Action classification algorithms to identify early child behaviors in a complex naturalistic setting were tested and validated using the video data. Decision making algorithms specific to the type of interactions seen in the GEAR system were developed to be used for robot automation. Conclusions:Preliminary results from this study support the feasibility of both the physical and cyber components of the GEAR system and demonstrate its potential for use in future studies to assess the effects on the codevelopment of the motor, cognitive, and social systems of very young children with mobility challenges.

This study describes infants’ behaviors with objects in relation to age, body position, and object properties. Object behaviors were assessed longitudinally in 22 healthy infants supine, prone, and sitting from birth through 2 years. Results reveal: (1) infants learn to become intense and sophisticated explorers within the first 6 months of life; (2) young infants dynamically and rapidly shift among a variety of behavioral combinations to gather information; (3) behaviors on objects develop along different trajectories so that behavioral profiles vary across time; (4) object behaviors are generally similar in supine and sitting but diminished in prone; and (5) infants begin matching certain behaviors to object properties as newborns. These data demonstrate how infants learn to match their emerging behaviors with changing positional constraints and object affordances.

Existing devices to assist upper extremity (UE) movement in infants with or at risk for motor impairments remain limited and are mainly passive devices. The aim of this project was to develop and assess the validity and reliability of the first-actuated wearable device for this population. A wearable device consisting of four pneumatic actuators (two per arm) was developed and tested on a custom-built physical model with articulated joints (four degrees-of-freedom (DOFs) per arm) based on an average 12-month-old infant's upper body. The device actively controls 2DOFs per arm (one at the elbow and one at the shoulder) and does not prohibit motion about the remaining non-actuated DOFs. Three distinct device actuator synergies, that resemble muscle recruitment strategies, were evaluated in a vertical reaching task using one arm and both arms. The device was assessed for its performance, wearability, and safety. Performance was assessed via the average duration, smoothness, and repeatability of reaching movements, and maximum range of motion per actuated joint. Wearability was assessed via kinematic compatibility to infant reaching trajectories. Safety was assessed via actuator durability. Results demonstrate the efficacy of the device and reveal key insights for further improvements.

The use of the open-area in-home BWSS was feasible for regular home use and associated with an increase in functional mobility for a child with spina bifida.

The purpose of this study was to test the effect of short-term training on reaching behavior in infants at the onset of reaching. The study was a single-blind, parallel group design, randomized controlled clinical trial. Thirty healthy infants were randomly assigned to a social control group (n = 15) or a reaching training group (n = 15). Infants began the study up to 3 days after the onset of reaching and were assessed three times across 2 days: pretraining (before training), posttraining 1 (after 1 session of training), and posttraining 2 (after 3 sessions of training). The reaching training group received 3 sessions of training by a physical therapist while the control group received a similar amount of time sitting in the therapist's lap. The data were analyzed using repeated-measures analyses of variance, and independent-samples tests with Bonferroni adjustments. Short-term training resulted in increased frequency of object contacts, shorter and smoother reaches, and improved hand positioning. The few short training sessions likely provided opportunities for infants to explore and learn to select movements from their existing movement repertoire. These results demonstrate that adaptive changes in infants' novel behaviors can emerge rapidly, and highlight the need for increased understanding of how to most effectively time early interventions.

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.