Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6465
• Main Authors: Yoshioka, Naohito, Takeuchi, Hiroki, Shu, Yuzhuo, Okamatsu, Taro, Araki, Nobuyuki, Kamakura, Yoshiyuki, Ohsuga, Mieko
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.10.2024; MDPI
• Subjects: Adult; Construction equipment industry; Construction Industry; cybersickness; Earthmoving equipment; Electrocardiography; Electrodes; Electroencephalography; Excavating machinery; Experiments; Humans; low-burden physiological measurement; Machine Learning; Male; Methods; Motion sickness; Motion Sickness - physiopathology; Nervous system; physiological measurement; Physiology; Questionnaires; Ratings & rankings; remote control; ROC Curve; visually induced motion sickness; Young Adult; Japan; cybersickness; low-burden physiological measurement; visually induced motion sickness; remote control; machine learning; physiological measurement; operator condition

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we d...