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 Kasım 2020 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/tiptekno50054.2020.9299316
  • Basıldığı Ülke: ELECTR NETWORK
  • Anahtar Kelimeler: Stress detection, pleasantness, machine learning, classification, pupil diameter, feature extraction
  • Ankara Üniversitesi Adresli: Evet

Özet

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.