Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanc...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (14), p.4642
Main Authors: Park, Sangin, Mun, Sungchul, Ha, Jihyeon, Kim, Laehyun
Format: Article
Language:English
Subjects:
Cameras
Cognitive load
Data processing
Disorientation
Experiments
Fatigue
Information processing
Infrared cameras
Measurement methods
Medical research
Motion sickness
Nausea
Nervous system
non-contact measurement
Physiology
pupillary rhythms
Questionnaires
Radial basis function
Rhythm
Skin
Support vector machines
Virtual reality
Webcams
Workloads
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Summary:Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21144642