Adaptive Humanoid Robots for Pain Management in Children

Abstract

Accurate pain assessment and management is particularly important in children exposed to prolonged or repeated acute pain including procedural pain because of elevated risk for adverse outcomes such as traumatic medical stress, intense pain response for subsequent pain and also developing chronic pain. Our current work in progress tries to help pain management in children through developing intelligent adaptive humanoid robots as a multi-modal non pharmacological intervention. Our current work increases the interactive capabilities of Nao humanoid robots by using the camera and microphone to assess pain and emotion in children undergoing procedural treatment through combining detection models for facial expression, voice quality, and adapt the robot s verbal and non-verbal interactive responses accordingly for optimal distraction through adaptive behavioral models. By combining two different methods of obtaining emotion predictive probabilities using facial expression and stress speech data, we predict an emotion label. This label is then used as an environment input to a reinforcement learning model with the robot as the agent to choose the best action out of a set of entertaining and distracting verbal and non-verbal actions to cheer up the child and distract them from the pain and fear of the medical procedure.