Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States

The HCI community is actively seeking novel methodologies to gain insight into the user’s experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies’ scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.

Mechatronic system for ABO human blood typing

In medical emergency situations, when a patient needs a blood transfusion, the universal blood type O− is administered. This procedure may lead to the depletion of stock reserves of O− blood. Nowadays, there is no commercial equipment capable of determining the patient's blood type in situ, in a fast and reliable process. Human blood typing is usually performed through the manual test, which involves a macroscopic observation and interpretation of the results by an analyst. This test, despite of having a fast response time, may lead to human errors, which sometimes can be fatal to the patient. This paper presents the development of an automatic mechatronic prototype for determining human blood typing (ABO and Rh systems) through image processing techniques. The prototype design takes into account the characteristics of reliability of analysis, portability, and response time allowing the system to be used in emergency situations. The developed prototype performs blood and reagents mixture acquires the resultant image and processes the data (based on image processing techniques) to determine the sample blood type. It was tested in a laboratory, using cataloged samples of blood types, provided by the Portuguese Institute of Blood and Transplantation. Hereafter, it is expected to test and validate the prototype in clinical environments.