Autonomous vehicles pose challenges regarding motion sickness (MS), as passengers engage in nondriving related tasks. To support the acceptance of self-driving cars, we evaluated effectiveness of a novel, noncontact tactile stimulation. The system provides anticipatory cues to passengers’ palms using modulated ultrasound. The effectiveness of the system was determined using both, electrodermal responses (EDRs) evoked by driving maneuvers and subjective MS assessments. We observed significantly increased amplitudes of EDRs with higher MS levels, a relationship established for the first time in this context. The implementation of our stimulation system resulted in significantly reduced EDR amplitudes, indicating its efficacy in mitigating arousal and MS symptoms. This research contributes to developing objective MS evaluation methods and minimally intrusive mitigation strategies, potentially improving passenger comfort and acceptance of self-driving vehicles.

With the arrival of the Fifth Industrial Revolution, human augmentation through assistive technologies is rapidly advancing. This article presents the first neuroergonomic assessment of a haptic vest as a human attention management device. The vest enables operators to monitor and respond to tasks outside their direct line of sight by leveraging the underutilized somatosensory channel, reducing overload on visual and auditory pathways while maintaining situational awareness. We assessed the vest in a simulated factory setting using objective electroencephalography measurements and subjective workload assessments. Frontal midline theta (Fm$\uptheta$) band power indicated mental effort, complemented by NASA task load index (TLX) ratings. Our results indicate a significant advantage for haptic cues over visual prompts, supported by improved NASA TLX scores and reduced Fm$\uptheta$power, which suggests a decrease in cognitive strain. This study integrates key human factors and offers a neuroergonomic framework for human augmentation in industrial environments, supporting the role of a haptic interface in enhancing human–computer interaction.

Due to high workload and often excessive working hours, team members in operating rooms perform surgical procedures under difficult conditions and often without sufficient breaks. Despite this, the team must deal with incomplete information and unexpected distractions. This requires a suitable level of attention and the ability to balance the demands of the task with available cognitive resources. Advances in measurement technology and data analysis in neurotechnology open up new possibilities for the assessment of attention processes. Increasingly complex and demanding surgeries, especially, could benefit from the application of neuroergonomic automated assistants to minimize distraction, stress, and fatigue, and to facilitate interactions between team members. Such assistants could improve performance via monitoring of cognitive and affective states as well as the implementation of suitable interventions strategies. Understanding the impact of distractions on performance, enhancing individuals’ resilience to distractions, and potentially employing artificial assistants to mitigate their effects are critical future goals. In order to support such future developments, a standard taxonomy of attention in the operating theater is needed, as is a broader consensus regarding the nature of distraction. Ideally, such a model would serve as a basis for comparison between studies conducted in different laboratories, and in principle could also be used to bridge the gap between the laboratory and the real scenario. Here, we propose the adoption of a model of attention previously shown to be effective for modeling levels of attention in immersion and describe its application in the surgical context.

Motion sickness is a phenomenon attracting increasing attention with the ever-growing popularity of highly automated driving. Understanding motion sickness is of significant interest in the context of self-driven vehicles because, in this case, all occupants of the vehicle are passengers and, therefore, more susceptible to motion sickness. In this article, we report the findings of a study wherein motion sickness was induced in 40% of the participants while driving in real-world conditions. By recording various psychophysiological parameters continuously (electrodermal activity, skin temperature, heart rate, and heart rate variability), we investigate the feasibility of using these to objectively assess motion sickness. Furthermore, the instantaneous physiological reactions of participants to unpleasant driving maneuvers are examined. The changes in the electrodermal activity show a strong correlation with the subjective ratings of motion sickness levels as reported by the participants. The phasic component of the electrodermal activity suggests differences between participants that are susceptible to motion sickness and those who are not. Several driving maneuvers (accelerations, cornering, and driving over speed bumps) were identified as events triggering significant electrodermal responses. These responses could be the result of a mismatch between visual and vestibular perception acting as an aversive, arousing stimulus. While, in this work, the driving maneuvers were partially overlapping and nonuniform, our results pave the way for future investigation of physiological responses to single driving events and their relation to motion sickness with the potential to identify real-time markers of possibly unpleasant driving maneuvers.

Drivers who assign the driving task to a self-driving car switch to a passive role to work or enjoy leisure time like a traditional passenger. Consequently, the risk of developing motion sickness (MS) symptoms increases significantly. Adapting one’s own driving behavior, e.g. by choosing an alternative route or decreasing the velocity, offers future intelligent vehicles a way to independently prevent MS. Accurate predictions help to improve journey’s planning of the vehicle and make correct decisions so as to minimize disruption to the traffic flow. In the present study our contribution is as follow: We conduct two studies by focusing on real-world driving under self-driving conditions and induced MS symptoms in passengers. We simulated driving parameters of the conducted studies to extract simulated driving dynamics and contrasted them with recorded driving dynamics. A well-known model of MS, namely the six-degrees-of-freedom subjective vertical conflict model (6DOF-SVC model) was utilized to predict motion sickness incidence (MSI) for both studies. In order to do so, we implemented a customized Human-Vehicle-Model to map the car’s dynamics to the head, which is crucial to apply the 6DOF-SVC model. We evaluated different Human-Vehicle-Model conditions and optimized the parameters of the 6DOF-SVC model to increase prediction accuracy in the case of our experiments. Note that our modeling approach enabled capture effects of missing visual anticipation and cognitive distraction that we present in our experiment. It is concluded that the 6DOF-SVC model is applicable in realistic driving scenarios as the ones used in our study.

The quantification of attention during driving can help identify situations in which the driver is not completely aware of the situation. By using the principle of phase-amplitude coupling (PAC) in electroencephalographic (EEG) signals, we aimed to test if PAC might be eligible as a biomarker of attention in multimodal tasks such as driving. Surface EEG was measured simultaneously in drivers and copilots while participating in simulated driving scenarios with varying multimodal attentional demands. The PAC between Theta-band phase and Gamma-band amplitude from the EEG was obtained and evaluated. Results showed significant PAC differences between drivers and copilots in areas related to multimodal attention (prefrontal cortex, frontal eye fields, primary motor cortex, and visual cortex). The results were confirmed by behavioral data acquired during the test (detection task). We conclude that PAC does function as a biomarker for attentional demand by detecting cortical areas being activated through specific multimodal (in this case, driving) tasks.

The theory of signal-adapted filter banks has been developed in signal compression in recent years and only rarely be applied to other applications fields such as machine learning. In this paper, we propose lattice structure based signal-adapted filter banks and time-scale atoms, respectively, for the construction of morphological local discriminant bases and hybrid wavelet-support vector classifiers. The first mentioned method is a more powerful construction of the recently introduced local discriminant bases algorithm which employs, in addition to the conventional wavelet-packet tree adjustment, an adaptation of the analyzing time-scale atoms. The latter mentioned method utilizes adapted wavelet decompositions which are tailored for support vector classifiers with radial basis functions as kernels. For both methods, we present applications in biomedical signal processing.