We present the design of a novel multidisciplinary system for the recognition, classification, and robotic kinetic expression of human emotions, towards more inclusive human-robot interaction. Overcoming the obstacle of designing social robots to communicate in an emotionally engaged manner remains an active challenge in HRI, impeding the development of fluid and natural human-robot communication, and many systems are still reliant upon specialist technical knowledge, literacy skills, or physical dexterity to support interaction. A multidisciplinary approach to this problem allows for the use of data-driven input to a robotic system to be combined with affective and expressive illustrated output, enhancing a robot’s capacity in both recognizing and potentially reciprocating emotional expression during HRI. Using an action research methodology, we adopted an existing facial recognition system, FaceOSC, to initially detect, and Wekinator to algorithmically train facial expression data to ideate and visualize human emotions through robotic gestures and illustrations. A robot arm, UFactory xArm 5, was kinematically trained to produce expressive gestures corresponding to the detected human emotions. The resultant movement of the robot is able to express and illustrate four primary emotions: anger, sadness, joy, and fear.