Detecting and predicting visually induced motion sickness with physiological measures in combination with machine learning techniques.

Visually induced motion sickness (VIMS) is a common sensation when using visual displays such as smartphones or Virtual Reality. In the present study, we investigated whether Machine Learning (ML) techniques in combination with physiological measures (ECG, EDA, EGG, respiration, body and skin temperature, and body movements) could be used to detect and predict the severity of VIMS in real-time, minute-by-minute. A total of 43 healthy younger adults (25 female) were exposed to a 15-minute VIMS-inducing video. VIMS severity was subjectively measured during the video using the Fast Motion Sickness Scale (FMS) as well as before and after the video using the Simulator Sickness Questionnaire (SSQ). Thirty-one participants (72%) experienced VIMS in the present study. Results showed that changes in facial skin temperature and body movement had the strongest relationship with VIMS. On a minute-by-minute basis, ML models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between sick and non-sick participants was found. Our findings suggest that physiological measures may be useful for measuring VIMS, but they are not a reliable standalone method to detect or predict VIMS severity in real-time.

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.