Emotion Analysis in Hospital Bedside Infotainment Platforms Using Speeded up Robust Features

Abstract

Far from the heartless aspect of bytes and bites, the field of affective computing investigates the emotional condition of human beings interacting with computers by means of sophisticated algorithms. Systems that integrate this technology in healthcare platforms allow doctors and medical staff to monitor the sentiments of their patients, while they are being treated in their private spaces. It is common knowledge that the emotional condition of patients is strongly connected to the healing process and their health. Therefore, being aware of the psychological peaks and troughs of a patient, provides the advantage of timely intervention by specialists or closely related kinsfolk. In this context, the developed approach describes an emotion analysis scheme which exploits the fast and consistent properties of the Speeded-Up Robust Features (SURF) algorithm in order to identify the existence of seven different sentiments in human faces. The whole functionality is provided as a web service for the healthcare platform during regular Web RTC video teleconference sessions between authorized medical personnel and patients. The paper discusses the technical details of the implementation and the incorporation of the proposed scheme and provides initial results of its accuracy and operation in practice.