The Affective Perceptual Model: Enhancing Communication Quality for Persons with PIMD

Methods for prolonged compassionate care for persons with Profound Intellectual and Multiple Disabilities (PIMD) require a rotating cast of import people in the subjects life in order to facilitate interaction with the external environment. As subjects continue to age, dependency on these people increases with complexity of communications while the quality of communication decreases. It is theorized that a machine learning (ML) system could replicate the attuning process and replace these people to promote independence. This thesis extends this idea to develop a conceptual and formal model and system prototype. The main contributions of this thesis are: (1) proposal of a conceptual and formal model for using machine learning to attune to unique communications from subjects with PIMD, (2) implementation of the system with both hardware and software components, and (3) modeling affect recognition in individuals based on the sensors from the hardware implementation.