Emotion and disposition detection in medical machines: Chances and challenges

Abstract

Machines designed for medical applications beyond usual data acquisition and processing need to cooperate with and adapt to humans in order to fulfill their supportive tasks. Technically, medical machines are therefore considered as affective systems, capable of detecting, assessing and adapting to emotional states and dispositional changes in users. One of the upcoming applications of affective systems is the use as supportive machines involved in the psychiatric disorder diagnose and therapy process. These machines have the additional requirement of being capable to control persuasive dialogues in order to obtain relevant patient data despite disadvantageous set-ups. These automated abilities of technical systems combined with enhanced processing, storage and observational capabilities raise both chances and challenges in medical applications. We focus on analyzing the objectivity, reliability and validity of current techniques used to determine the emotional states of speakers from speech and the arising implications. We discuss the underlying technical and psychological models and analyze recent machine assessment results of emotional states obtained through dialogues. Conclusively we discuss the involvement of affective systems as medical machines in the psychiatric diagnostics process and therapy sessions with respect to the technical and ethical circumstances.