Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors

Introduction: It is crucial to identify neurodevelopmental disorders in infants early on for timely intervention to improve their long-term outcomes. Combining natural play with quantitative measurements of developmental milestones can be an effective way to swiftly and efficiently detect infants who are at risk of neurodevelopmental delays. Clinical studies have established differences in toy interaction behaviors between full-term infants and pre-term infants who are at risk for cerebral palsy and other developmental disorders. Methods: The proposed toy aims to improve the quantitative assessment of infant-toy interactions and fully automate the process of detecting those infants at risk of developing motor delays. This paper describes the design and development of a toy that uniquely utilizes a collection of soft lossy force sensors which are developed using optical fibers to gather play interaction data from infants laying supine in a gym. An example interaction database was created by having 15 adults complete a total of 2480 interactions with the toy consisting of 620 touches, 620 punches-"kick substitute," 620 weak grasps and 620 strong grasps. Results: The data is analyzed for patterns of interaction with the toy face using a machine learning model developed to classify the four interactions present in the database. Results indicate that the configuration of 6 soft force sensors on the face created unique activation patterns. Discussion: The machine learning algorithm was able to identify the distinct action types from the data, suggesting the potential usability of the toy. Next steps involve sensorizing the entire toy and testing with infants.

Design of pediatric robot to simulate infant biomechanics for neuro-developmental assessment in a sensorized gym

Infants at risk for developmental delays often exhibit postures and movements that may provide a window into potential impairment for cerebral palsy and other neuromotor conditions. We developed a simple 4 DOF robot pediatric simulator to help provide insight into how infant kinematic movements may affect the center of pressure (COP), a common measure thought to be sensitive to neuromotor delay when assessed from supine infants at play. We conducted two experiments: 1) we compared changes in COP caused by limb movements to a human infant and 2) we determined if we could predict COP position due to limb movements using simulator kinematic pose retrieved from video and a sensorized mat. Our results indicate that the limb movements alone were not sufficient to mimic the COP in a human infant. In addition, we show that given a robot simulator and a simple camera, we can predict COP measured by a force sensing mat. Future directions suggest a more complex robot is needed such as one that may include trunk DOF.