Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users.

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24.41 % in accuracy and of 27.97 % in F1 score can be achieved even at high activity levels.

Weighted Pointer: Error-aware Gaze-based Interaction through Fallback Modalities.

Gaze-based interaction is a fast and ergonomic type of hands-free interaction that is often used with augmented and virtual reality when pointing at targets. Such interaction, however, can be cumbersome whenever user, tracking, or environmental factors cause eye tracking errors. Recent research has suggested that fallback modalities could be leveraged to ensure stable interaction irrespective of the current level of eye tracking error. This work thus presents Weighted Pointer interaction, a collection of error-aware pointing techniques that determine whether pointing should be performed by gaze, a fallback modality, or a combination of the two, depending on the level of eye tracking error that is present. These techniques enable users to accurately point at targets when eye tracking is accurate and inaccurate. A virtual reality target selection study demonstrated that Weighted Pointer techniques were more performant and preferred over techniques that required the use of manual modality switching.

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research.

With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity.

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems that may in turn help to mitigate human error in complex operations. The challenge of this endeavor lies with diagnosticity. Despite different underlying causes, the physiological correlates of workload and acute psychological stress manifest in rather similar ways and can be easily confounded. The current work aimed to build a diagnostic model of mental state through the simultaneous classification of mental workload (varied through three levels of the n-back task) and acute stress (the presence/absence of aversive sounds) with machine learning. Using functional near infrared spectroscopy (fNIRS) and electrocardiography (ECG), the model's classifiers was above-chance to disentangle variations of mental workload from variations of acute stress. Both ECG and fNIRS could predict mental workload level, the best accuracy resulted from the two measures in combination. Stress level could not be accurately diagnosed through ECG alone, only with fNIRS or ECG and fNIRS combined. Individual calibration may be important since stress classification was more accurate for those with higher subjective state anxiety, perhaps due to a greater sensitivity to stress. Mental workload and stress were both better classified with activity in lateral prefrontal regions of the cortex than the medial areas, and the HbO2 signal generally lead to better classification than HHB. The current model represents a step forward to finely discriminate different mental states despite their rather analog physiological correlates.

Eyes have ears: Indexing the orienting response to sound using pupillometry.

The rare occurrence of a sound deviating from the auditory background tends to trigger attentional orienting. While some sympathetic physiological responses can be used to index this orienting response, findings surrounding the pupillary dilation response (PDR) as a proxy for the orienting response are conflicting. The current study was tailor-designed to examine whether the PDR satisfies specific criteria of an orienting response index, namely the classic habituation pattern and a sensitivity to the size of the deviation. The PDR decrement to a repeated standard sound, recovery to a deviant sound, and dishabituation to the re-presentation of the standard were assessed for small and large deviations embedded in irrelevant auditory sequences. The PDR not only showed habituation and dishabituation, but also recovered in correspondence with the magnitude of the acoustic deviation. This consistency between variations of the PDR and orienting response's properties indicates that the PDR is a valid index of the auditory orienting response.