Data mining and knowledge discovery from physiological sensors

We present in this paper our method for discovering logic rules from physiological data by applying a fusion of preprocessing and
data mining using decision trees. We have focused on four sensors
representative of wearable technology capabilities namely;
plethysmography, galvanic skin response, respiration, and body
temperature, sourced from a highly cited dataset in Affective
Computing. The method involved generating a number of datasets
from the physiological data and subsequently performing
classification using the C4.5 decision tree algorithm with a focus
on knowledge discovery. The findings of this research demonstrate
that preprocessing data into three classes with extreme boundaries
and a neutral class, as well as classifying the two classes without
the neutral class can produce high-quality rules. The discovered
knowledge in the form of the top 4 rules was mapped on the valence
and arousal emotional model. Finally, these rules are interpreted
with the aid of box and whisker plots in the context of the
underlying physiological processes.