Based on sensory conflict theory, motion sickness is strongly related to the information processing capacity or resources of the brain to cope with the multi-sensory stimuli experienced by watching virtual reality (VR) content. The purpose of this research was to develop a method of measuring motion sickness using the heart-evoked potential (HEP) phenomenon and propose new indicators for evaluating motion sickness. Twenty-eight undergraduate volunteers of both genders (14 females) participated in this study by watching VR content on both 2D and head-mounted devices (HMD) for 15 min. The responses of HEP measures such as alpha power, latency, and amplitude of first and second HEP components were compared using paired t-tests and ANCOVA. This study confirmed that motion sickness leads to a decline in cognitive processing, as demonstrated by increasing in alpha power of HEP. Also, the proposed indicators such as latency and amplitude of the HEP waveform showed significant differences during the experience of motion sickness and exhibited high correlations with alpha power measures. Latencies of the first HEP component, in particular, are recommended as better quantitative evaluators of motion sickness than other measures, following the multitrait-multimethod matrix. The proposed model for motion sickness was implemented in a support vector machine with a radial basis function kernel, and validated on twenty new participants. The accuracy, F1 score, precision, recall, and area under the curve (AUC) of the motion-sickness classification results were 0.875, 0.865, 0.941, 0.8, and 0.962, respectively.

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence.

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.

The event-related potential (ERP) technique is widely used in various fields, but the requirement for sensor attachment limits its application. The aim of this study was to develop an infrared (IR) webcam-based, non-contact system to obtain the amplitudes and latencies of ERP via measurement of event-related pupillary responses (ErPRs) from pupillary rhythms. A total of 32 healthy volunteers participated in this study, and they were required to perform the three levels of mental arithmetic tasks to induce mental workloads (MWLs). ERPs and ErPR were measured by ERP task for the different MWL states and compared based on statistical analysis, classification, correlation, and Bland–Altman plot. Both ERP and ErPR amplitudes and latencies for the three MWL states were revealed by one-way repeated measures analysis of variance to be statistically significantly different. Furthermore, the ERP and ErPR data were found to have 73 and 80% classification performances, respectively, using k-nearest neighbour (10-fold cross validation, n = 96). Correlation coefficients between ERP and ErPR features, which were in the range of 0.642–0.734, indicated good or strong correlation, and good agreement between the indices from these two types of experimental measurement indices was apparent in Bland–Altman plots. An advanced method for IR-webcam-based non-contact determination of ERP components via ErPR measurement was successfully developed, and it was demonstrated that this technique can be used for ERP component monitoring using a low-cost, non-invasive, and easily implemented IR webcam without the burden of sensor attachment.