Comparison of Several Machine Learning Classifiers for Arousal Classification: A Preliminary study

ERKUŞ E. C., PURUTÇUOĞLU GAZİ V., ARI F., Gokcay D.

2020 Medical Technologies Congress (TIPTEKNO), ELECTR NETWORK, 19 - 20 November 2020, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/tiptekno50054.2020.9299316
  • Country: ELECTR NETWORK
  • Keywords: Stress detection, pleasantness, machine learning, classification, pupil diameter, feature extraction
  • Ankara University Affiliated: Yes

Abstract

Detection of arousal intervals, especially stress detection via a human-machine interface is a trending topic. Stress detection algorithms with high accuracy can he used in many fields such as criminal interrogations or a variety of stress-related experiments. There are many indicators of the stress on the human body, especially on the face area, such as galvanic skin response (GSR), pupil diameter, heart rate (HR), and electromyography (EMG). Hereby, the measurement of such physiological data in stressful, joyful and non-stressful cases can reveal the effects of the stress on the body signals.