Transforming Physiological Data from a Generic Sensor to a Specialised One for Affect Detection

Abstract

Continuous reduction on hardware costs has been bringing the opportunity to employ cheap sensors to measure physiological data. However, this comes at a price of capturing some noisy information, which most likely would compromise both analysis and interpretation of the raw results. This paper investigates the reliability of a generic and a specialised sensor on capturing heart rate data and the challenge of extracting meaningful information from it for affect detection. A controlled stimulus in a laboratory setting is performed, in which participants play different levels of the game Tetris while their signal variations are observed. Since only the generic sensor does not reproduce the expected behaviour, filtering techniques are proposed to approximate its signal to the specialised one. Experimental results confirm that this goal is achieved by applying either “Grubbs' test”' for outliers detection or “three-sigma rule”. Such transformations highlight the need of filtering techniques for affective computing because they avoid misinterpretation of the results, as well as it represents a starting point towards finding a ground-truth to link possible user affect and physiological data.

Keywords: Affective Computing, Physiological Data, Sensor Comparisons, Emotions, Ground-truth Data Set

DOI: 10.54941/ahfe100545