Soft robotics hold promise in the development of safe yet powered assistive wearable devices for infants. Key to this is the development of closed-loop controllers that can help regulate pneumatic pressure in the device's actuators in an effort to induce controlled motion at the user's limbs and be able to track different types of trajectories. This work develops a controller for soft pneumatic actuators aimed to power a pediatric soft wearable robotic device prototype for upper extremity motion assistance. The controller tracks desired trajectories for a system of soft pneumatic actuators supporting two-degree-of-freedom shoulder joint motion on an infant-sized engineered mannequin. The degrees of freedom assisted by the actuators are equivalent to shoulder motion (abduction/adduction and flexion/extension). Embedded inertial measurement unit sensors provide real-time joint feedback. Experimental data from performing reaching tasks using the engineered mannequin are obtained and compared against ground truth to evaluate the performance of the developed controller. Results reveal the proposed controller leads to accurate trajectory tracking performance across a variety of shoulder joint motions.

Infant-robot interaction has been increasingly gaining attention, yet, there are limited studies on the development of robot-assisted environments that promote perceptual-motor development in infants. This paper assesses the feasibility of operating a spherical mobile robot, Sphero, to engage infants in perceptual-motor exploration of an open area. Two case scenarios were considered. In the first case, Sphero was the only robot providing stimuli in the environment. In the second case, two additional robots provided stimuli along with Sphero. Pilot data from two infants were analyzed to extract information on their visual attention to and physical interaction with Sphero, as well as their motor actions. Overall, infants (i) expressed a preference to Sphero regardless of stimulation levels, and (ii) moved out of stationary postures in an effort to chase and approach Sphero. These preliminary findings provide support for the future implementation of Sphero in robot-assisted learning environments to promote perceptual-motor development in infants.

This work focuses on closed-loop control based on proprioceptive feedback for a pneumatically-actuated soft wearable device aimed at future support of infant reaching tasks. The device comprises two soft pneumatic actuators (one textile-based and one silicone-casted) actively controlling two degrees-of-freedom per arm (shoulder adduction/abduction and elbow flexion/extension, respectively). Inertial measurement units (IMUs) attached to the wearable device provide real-time joint angle feedback. Device kinematics analysis is informed by anthropometric data from infants (arm lengths) reported in the literature. Range of motion and muscle co-activation patterns in infant reaching are considered to derive desired trajectories for the device’s end-effector. Then, a proportional-derivative controller is developed to regulate the pressure inside the actuators and in turn move the arm along desired setpoints within the reachable workspace. Experimental results on tracking desired arm trajectories using an engineered mannequin are presented, demonstrating that the proposed controller can help guide the mannequin’s wrist to the desired setpoints.

This paper presents the design and assessment of a fabric-based soft pneumatic actuator with low pressurization requirements for actuation making it suitable for upper extremity assistive devices for infants. The goal is to support shoulder abduction and adduction without prohibiting motion in other planes or obstructing elbow joint motion. First, the performance of a family of actuator designs with internal air cells is explored via simulation. The actuators are parameterized by the number of cells and their width. Physically viable actuator variants identified through the simulation are further tested via hardware experiments. Two designs are selected and tested on a custom-built physical model based on an infant’s body anthropometrics. Comparisons between force exerted to lift the arm, movement smoothness, path length and maximum shoulder angle reached inform which design is better suited for its use as an actuator for pediatric wearable assistive devices, along with other insights for future work.

Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. Existing human action recognition algorithms focus on adult applications rather than pediatric ones. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras. We develop an annotated dataset that includes diverse reaches performed while in a sitting posture by different infants in unconstrained environments (e.g., in home settings, etc.). Our approach uses the spatial and temporal connection of annotated bounding boxes to interpret onset and offset of reaching, and to detect a complete reaching action. We evaluate the efficiency of our proposed approach and compare its performance against other learning-based network structures in terms of capability of capturing temporal inter-dependencies and accuracy of detection of reaching onset and offset. Results indicate our BabyNet can attain solid performance in terms of (average) testing accuracy that exceeds that of other larger networks, and can hence serve as a light-weight data-driven framework for video-based infant reaching action recognition.

Providing infants with opportunities to engage in a variety of motor actions early in life impacts their later development. Our recent work utilizes socially assistive robots (SARs) and body weight support (BWS) technology to provide infants such opportunities. This paper examines the motor actions demonstrated by an infant with Down syndrome during their spontaneous interactions with the SARs and while using BWS assistance. The infant participated in eight 1-hour sessions over the course of four weeks. By using methodological tools from social networks, we identify and describe the complex nature of the infant’s motor actions displayed during interaction with both types of technology over time. The changes in these networks informs the development of robot-assisted learning environments that can be applied at this critical life period.

Activity recognition in children and infants is important in applications such as safety monitoring, behavior assessment, and child-robot interaction, among others. However, it differs from activity recognition in adults not only because body poses and proportions are different, but also because of the way in which actions are performed. This paper addresses the problem of infant action classification in challenging conditions. The actions are performed in a pediatric rehabilitation environment in which not only infants but also robots and adults are present, with the infant being one of the smallest actors in the scene. We propose a multiview action classification system based on Faster R-CNN and LSTM networks, which fuses information from different views by using learnable fusion coefficients derived from detection confidence scores. The proposed system is view-independent, learns features that are close to view-invariant, and can handle new or missing views at test time. Our approach outperforms the state-of-the-art baseline model for a small dataset (2 subjects, 10-24 months old) by 11.4% in terms of average classification accuracy in four classes (crawl, sit, stand and walk). Moreover, experiments in an extended dataset (6 subjects, 8-24 months old) show that the proposed fusion strategy outperforms all the alternative fusion methods studied.