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Despite extensive research, measurable benefits of predictive policing are scarce. We argue that powerful models might not always help the work of officers. Furthermore, developed models are often unexplainable, leading to trust issues between police intuition and machine-made prediction. We use a joint approach, mixing criminology and data science knowledge, to design an explainable predictive policing model. The proposed model (a set of explainable decision trees) can predict police resource requirement across the city and explain this prediction based on human-understandable cues (i.e., past event information, weather, and socio-demographic information). The explainable decision tree is then compared to a non-explainable model (i.e., a neural network) to compare performance. Analyzing the decision tree behaviour revealed multiple relations with established criminology knowledge. Weather and recent event distribution were found to be the most useful predictors of police workforce resource. Despite wide research showing relationships between socio-demographic information and police activity, socio-demographic information did not contribute much to the model’s performance. Though there is a lack of research on measurable effects of predictive policing applications, we argue that combining human instinct with machine prediction reduces risks of human knowledge loss, machine bias, and lack of confidence in the system.

Britain has always seemed to be the supreme ruler of the seas, so it might seem surprising that the British were falling behind when it came to ferrying passengers across the Atlantic in the early nineteenth century. Britain had found itself in second place to the Americans by the mid 1830s when it came to shipbuilding and cargo contracts; their shipbuilding techniques were outdated, which drove passengers to the larger and safer American built packets, and were sailing without fixed schedules, often finding themselves days or weeks behind schedule, often spoiling goods and upsetting passengers. Their national pride was at stake. This essay shows how a wealthy Canadian shipowner by the name of Samuel Cunard rewrote not only the rules of how shipowners managed their fleets, but also rejuvenated Britain’s maritime glory.

This work explores the design of marking menus for gaze-based AR/VR menu selection by expert and novice users. It first identifies and explains the challenges inherent in ocular motor control and current eye tracking hardware, including overshooting, incorrect selections, and false activations. Through three empirical studies, we optimized and validated design parameters to mitigate these errors while reducing completion time, task load, and eye fatigue. Based on the findings from these studies, we derived a set of design guidelines to support gaze-based marking menus in AR/VR. To overcome the overshoot errors found with eye-based expert marking menu behaviour, we developed StickyPie, a marking menu technique that enables scale-independent marking input by estimating saccade landing positions. An evaluation of StickyPie revealed that StickyPie was easier to learn than the traditional technique (i.e., RegularPie) and was 10% more efficient after 3 sessions.

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 ofPhysicalActivity andStresScollected 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 athttp://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

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 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.

Assessment of mental workload is crucial in safety-critical applications. Often, such applications require the user to be ambulant, such as first responders (e.g., paramedics, firefighters, or police officers). Typically, mental workload models have relied on electroencephalography (EEG) signals. EEGs, however, are known to be highly sensitive to movement artefacts, thus limited applications exist for ambulant users and studies have mostly occurred in controlled laboratory settings. In this paper, we explore the robustness of new non-linear features against movement artefacts and test their effectiveness in monitoring mental workload for ambulant users with the end goal of developing mitigation measures based on mental state of operators. To this end, an EEG experiment was conducted where mental workload and physical activity levels were modulated simultaneously and data was collected from 48 participants. Classical EEG features used for workload assessment, such as spectral power and amplitude/phase coherence, were used as benchmarks and compared against the proposed non-linear multi-scale permutation entropy features. Experimental results show the proposed features consistently outperforming the benchmark ones, thus high-lighting their robustness to movement artefacts.

Stress is well known to have negative effects on health and workplace performance. Physiological sensing using wearables shows in turn great potential for realtime stress monitoring. While some off-the-shelf consumer products (e.g. smartwatches) already feature stress detection, there is still a pressing need to improve the robustness of these models in ecological settings where physical activity can hamper detection accuracy. In this paper, we show that using a multimodal physiological stress model can not only improve model accuracy, but can increase robustness to physical activity inference. To do so, we propose a video game based method to elicit emotional responses. More specifically, 48 participants played video games in which psychological stress and physical activity were jointly modulated. Physiological features showing robustness to stress are analyzed in order to guide further research.

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.

Research on adaptive systems based on human psychophysiological assessments has been growing rapidly over the last decade. One fundamental component of such a system is human state assessment, on which the adaptation depends, at least partly. This is critical as the confidence of operators in such system will be determined to some extent by the accuracy of the models responsible for the assessment. This chapter presents work carried out in order to better understand the relationship between bio-behavioral data, psychological state, and operational performance of the operators. Modeling physiological parameters and performance was performed through a manipulation of three factors. The first factor was the size of smoothing window for performance. The second factor was the performance decrement threshold for labelling functional and sub-functional states. Finally, the third factor was the mode of classification being either prospective or descriptive. We used two types of classifiers, a linear and a non-linear classifier, and compared performance. Insights emerging from this work support that the use of multiple sources of bio-behavioral data, combined in a non-linear fashion, increases the psychometric qualities of state classifiers. This suggests that if such systems are to be used in safety-critical systems, they should be implemented using a wide variety of sensors to increase classification accuracies of performance.

Finding a physiological signature of a player’s fun is a goal yet to be achieved in the field of adaptive gaming. The research presented in this paper tackles this issue by gathering physiological, behavioural and self-report data from over 200 participants who played off-the-shelf video games from the Assassin’s Creed series within a minimally invasive laboratory environment. By leveraging machine learning techniques the prediction of the player’s fun from its physiological and behavioural markers becomes a possibility. They provide clues as to which signals are the most relevant in establishing a physiological signature of the fun factor by providing an important score based on the predictive power of each signal. Identifying those markers and their impact will prove crucial in the development of adaptive video games. Adaptive games tailor their gameplay to the affective state of a player in order to deliver the optimal gaming experience. Indeed, an adaptive video game needs a continuous reading of the fun level to be able to respond to these changing fun levels in real time. While the predictive power of the presented classifier remains limited with a gain in the F1 score of 15% against random chance, it brings insight as to which physiological features might be the most informative for further analysis and discuss means by which low accuracy classification could still improve gaming experience.

Assessment of mental workload is crucial for applications which require constant attention and where conditions such as mental fatigue and drowsiness must be avoided. As such, electroencephalography (EEG) based mental workload models have been developed in the past. The majority of these models, however, have assumed individuals are not ambulant, thus bypassing the issue of movement-related EEG artefacts. While such models may be useful for a number of applications (e.g., operators are sitting), they may not apply in situations in which operators are performing their task under different physical activity levels. Representative examples can include first responders, such as paramedics, firefighters, or police officers. In this work, we take the first steps towards overcoming this limitation and present results of an experiment simultaneously eliciting increasing mental workload states at varying physical activity levels. EEG data from forty-seven participants was collected while they performed the NASA Revised Multi-Attribute Task Battery II (MATB-II) under three different activity level conditions (no, medium, high). In this study, we report the effects of activity on the noise-robustness and distribution of several spectral, amplitude/phase coherence, and amplitude modulation features, with the ultimate goal of deriving a feature set tailored towards automated workload assessment during physical activity. Preliminary results show spectral features acquired from the frontal area of the cortex as the most promising and that activity aware mental workload models should be developed.

A key goal of neuroergonomics is to develop tools to measure mental workload during task execution, potentially enabling safer and more efficient human–machine interaction in diverse working environments. A promising approach to developing these high-performance and robust cognitive monitoring systems is to combine neurophysiological modalities, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), that can offer complementary information on brain activity. Here, innovative temporal dynamics-related EEG and fNIRS features are described that will enable development of highly effective multimodal mental-workload measures.

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.

The key to the development of adaptive gameplay is the capability to monitor and predict in real time the players experience (or, herein, fun factor). To achieve this goal, we rely on biometrics and machine learning algorithms to capture a physiological signature that reflects the player's affective state during the game. In this paper, we report research and development effort into the real time monitoring of the player's level of fun during a commercially available video game session using physiological signals. The use of a triple-classifier system allows the transformation of players' physiological responses and their fluctuation into a single yet multifaceted measure of fun, using a non-linear gameplay. Our results suggest that cardiac and respiratory activities provide the best predictive power. Moreover, the level of performance reached when classifying the level of fun (70% accuracy) shows that the use of machine learning approaches with physiological measures can contribute to predicting players experience in an objective manner.

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Understanding the propagation of RF energy in confined naval shipboard environments is extremely important to the operability and effectiveness of communication systems used by the Navy. Single-frequency communication systems are known to suffer from multipath interference that can render them ineffective in confined spaces. Spread-spectrum and frequency-hopping systems, currently used in wireless local area networks (LANs), have been shown to operate effectively in confined shipboard environments in the absence of other RF signals. However, the transfer characteristics of these signals relative to bulkheads, watertight doors, ducts, mast cable transits, compartment penetrations and obstructions, and steel deck plates are not well characterized. Also, the complex interactions of communication signals with competing RF signals are not well understood for confined ship spaces. To address these issues for systems between 800 MHz and 3 GHz, a broadband bistatic measurement system was designed, and initial measurements were taken on the ex-USS Shadwell (23-25 February 1998) in Mobile, Alabama. More than 100 sets of data were collected for various open/closed door configurations on three levels of the ex-USS Shadwell. The measurement system and the measurements are discussed. Initial observations indicate that received power levels over reasonably long propagation distances were sufficient to ensure communication, even when all closures were set.