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Towards Long-Term Learning to Motivate Spontaneous Infant Kicking for Studies in Early Detection of Cerebral Palsy using a Robotic System: A Preliminary Study

Infant kicking patterns can provide clues for causes for concern with future development. Cerebral Palsy is a development disorder that may be predicted by observing the spontaneous kicking patterns of an infant. Early detection and intervention can improve the overall long term outcome through specific physical therapy exercises. Since all infants are unique it may be beneficial to learn child specific stimuli that optimize the quantity of kicking actions. Discovering the stimuli that will encourage a particular infant to perform kicking actions will give healthcare professionals more opportunities to observe and evaluate these actions for possible atypical patterns. We expand on previous work that utilizes computer vision and a robotic baby mobile that detects infant kicking motions and activates stimuli to encourage continued kicking. Based on the observed state-action pairs recorded while an infant interacts with the robotic baby mobile, we develop a Markov Decision Process and calculate an optimal policy to encourage an increased amount of kicking. This method could theoretically be applied to different infants, which would result in varying optimal policies that are specific to each child. In this paper we will briefly describe the robotic system, discuss the resulting Markov Decision Process and optimal policy, and describe future works.