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Sensor-Hub: A Real-Time Data Integration and Processing Nexus for Adaptive C2 Systems
Abstract
The present paper introduces the Sensor-Hub, a prototype tool for augmenting the common operating picture and adaptability of distributed teams in safety-critical environments. The Sensor-Hub aims to facilitate the integration and interpretation of data collected directly from humans augmented with sensing capability involved in the situation to produce timely and relevant information on the current functional state of operators, the situation and their environment. Herein, we elaborate on the development and validation of the sensing and interpretation framework, emphasising the key adaptation capabilities that it seeks to enable. Lastly, this paper illustrates three sectors of application of the Sensor-Hub: training of safety-critical team operations, real-time error-prevention and adaptation during operations, and assessment of inter-agent and human-technology interactions.
... To ensure adaptive control, information about the operator is required. The proposed approaches build on the existing Sensorhub Toolkit, an edge-computing platform for C2 Command and Control Operations as demonstrated in [30] previously used on on-foot personnel and aviation operators [31][32]. The Sensorhub module enables the interpretation in real-time of human-operator mental and physical states through the use of devices such as smart-shirt Hexoskin (monitoring heart rate variability, respiration rate), Conscious labs EEG headset, and more. ...
... For the pre-processing of the EEG signal a high-pass filter of 1 Hz and low-pass of 45 Hz was used which allowed the extraction of Power Spectral Density (PSD). PSD provides the power of five frequencies sub-bands: delta δ (1-4 Hz), theta θ (4-7 Hz), alpha α (8-12 Hz), beta β (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma γ (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45); which are often used to assess mental states such as workload, fatigue. Lastly, Fast Fourier Transform (FFT) was applied to the PSD band to transform the signal from time domain to the frequency domain and implement spectral analysis. ...
... For the pre-processing of the EEG signal a high-pass filter of 1 Hz and low-pass of 45 Hz was used which allowed the extraction of Power Spectral Density (PSD). PSD provides the power of five frequencies sub-bands: delta δ (1-4 Hz), theta θ (4-7 Hz), alpha α (8-12 Hz), beta β (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma γ (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45); which are often used to assess mental states such as workload, fatigue. Lastly, Fast Fourier Transform (FFT) was applied to the PSD band to transform the signal from time domain to the frequency domain and implement spectral analysis. ...
... To do so, we performed a flexible extraction of the model through the Open Neural Network Exchange (ONNX) format (a standard format for edge integration of machine-learning models). Then we integrated the model into the Sensor Hub solution [28], developed by Thales Research and Technology Canada, a real-time sensor-agnostic data integration, synchronization, and processing nexus that allows sampling data from multiple sensors, on multiple users simultaneously in operational use cases (see, e.g., [29]). ...
... The spectral power features measured with the PSD analysis allowed to provide the power in specific EEG frequency bands (e.g., [34]). Within the analysis, five frequency sub-bands were considered: delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Then, a Fast Fourier Transform (FFT) was used within the PSD for each electrode. ...
... The models generated were compared and the one with best performance was selected for extraction in ONNX format and implemented for the real-time assessment of the mental workload level. This integration was performed into the Sensor Hub solution [28]. The integration enables running the prediction model in real time in order to provide live assessments of the cognitive workload of an operator in real-life settings using real-time EEG data collected from the Conscious Lab headset. ...
Human errors are reputed to be one of the main causes of most accidents or incidents in aviation. Such can be explained by the fact that pilots are frequently exposed to sources of emotional and cognitive stressors, including challenges pertaining to mental workload. Real-time mental workload assessment of crew during flight could help identifying cognitive overload of the crew and then reduce aviation accidents. Electroencephalography (EEG) is a well-known tool used to infer mental states. EEG prediction of mental workload is however mostly performed in highly controlled settings whereas, in the cockpit, many types of confounds (e.g., physical movement) can induce noise into the signal. The goal of this study was to develop an EEG-based workload prediction model that could be used in aviation use cases characterized by noisy signal. To reach this goal, we used machine-learning algorithms to explore the feasibility to classify different levels of mental workload from EEG features. We used a dataset composed of noise induced by physical activity (either low, medium or high levels) collected while participants performed either low-demanding or high-demanding cognitive tasks. Using only three electrodes (Fp1, Fp2 and P3) and 15 spectral band features with the physical condition label, we generated a random forest classifier with a prediction accuracy of 76%. This model could run in real time and provide workload inference at a rate of one prediction each second. Overall, our results show the possibility for predicting real-time mental workload in operational environments using dry-electrode EEG solutions developed for ambulatory use cases.
... The solution developed relied on a set of sensors including: a smart garment with electrocardiography (ECG), respiration and acceleration components; a smartwatch with notification, accelerometer and monitoring capacities; a wearable recording functional near-infrared spectroscopy; and a phone equipped with a GPS, a camera and a microphone. Using the Sensor Hub solution (i.e. a multi-sensor system for near real-time monitoring; Gagnon et al., 2014), data collected from these sensors can be persisted and processed to extract features for the real-time prediction based on an ensemble of machine learning models. As outlined by subject-matter experts from two Canadian public safety organizations, such models should be able to predict and depict stress levels of trainees. ...
Stress can be an indicator of discomfort with a task, which is of relevance for training in safety-critical fields. Knowing a trainee’s stress level could be especially useful when objective performance outcomes are unclear or when success in training tasks alone is insufficient to predict proficiency in real-life safety-critical scenarios. In this study, stress classification models trained on open-access physiological data and integrated in Sensor Hub, a multi-sensor system for near real-time monitoring, were developed. To obtain ground-truth neurophysiological data recorded under high-stress conditions, raw electrocardiogram (ECG) and respiration data in an open-access database sourced from PhysioNet, consisting of 57 participants with arachnophobia watching spider videos, was used. Machine learning algorithms were trained on features extracted from these raw signals. A first set of algorithms focused on heart rate, respiratory rate, and heart rate variability (HRV) features. The second set included feature normalization according to an individual’s baseline. Models based on individually normalized features reached balanced prediction accuracy >80%. A pilot data collection was conducted with a different sensing device than the device used to obtain these measures. Qualitative analysis revealed that real-time R-R intervals from the new sensors were sensitive to artifacts, suggesting that the model relying on HRV features may not be reliable. The model that used only the baseline normalized heart and respiratory rate was selected as the final choice, exported in the Open Neural Network Exchange format and integrated into the Sensor Hub platform, providing predictions every second. This research demonstrates the potential of open-access data for providing a solid starting point for training cognitive models, while also highlighting the necessity of real-time testing to confirm that models can generalize across different sensors and processing pipelines.
... Besides, this data must be collected and analysed in real time or near-real time to provide a portrait of the state of the operator that is up to date and representative of their current state. To this end, our previous work led to the development of the Sensor Hub, a real-time data integration, synchronization, and processing nexus that allows the sampling of data from multiple sensors, on multiple users simultaneously (Gagnon et al. 2014). This technology allows more particularly the following: ...
Recent developments in sensing technologies make it increasingly feasible to collect physiological and behavioural data that can be exploited to understand operators’ cognitive challenges, health and operational readiness in real-life situations. Our previous work led to the development of a real-time data integration, synchronization, and processing nexus that can be used with multiple sensors and multiple users simultaneously. In turn, this data can be analysed and displayed on a dashboard to monitor one’s state using machine-learning derived or classical algorithms. This study presents how user-centred design can be harnessed to develop context-adapted monitoring solutions in two different use cases, that is space medicine and public safety personnel training. We highlight the steps taken to define context-adapted solutions for the exploitation of physiological and behavioural data. We also outline the necessity to consider end-users and stakeholders to produce usable information that is context relevant and that optimizes the human-system interaction.
... The two broad categories of interest are physical (e.g., athletic, medical) and psychological (e.g., emotive, cognitive). Assessment of OFS is useful to support decision makers and inform on the level of readiness [12]. ...
... The two broad categories of interest are physical (e.g., athletic, medical) and psychological (e.g., emotive, cognitive). Assessment of OFS is useful to support decision makers and inform on the level of readiness [12]. ...
There is potential great value in turning physiological and behavioral data into actionable information in tactical environments. However, the design of an appropriate system in terms of measurement accuracy, wearing comfort and technical feasibility, for instance for forces training in realistic conditions, therefore requires addressing multiple scientific and technical challenges. The current paper focuses on work realized to address four challenges identified in recent efforts in the development of an integrated wearable system. These four challenges pertain to: 1) Data management; 2) Wearable sensors; 3) Algorithms and models; and 4) Human factors considerations. Components were developed and integrated to tackle each of the four challenges and the integrated system was tested on participants during field trials. The system referred to "Readiness Evaluation: QUantified INdividuals" (REQUIN), offers a dashboard to visualize in real time the various metrics collected and calculated by the system, including metrics derived from wearable sensors and models. Results of the field trials are discussed in regard with the four challenges addressed in the paper and recommendations for further research are presented, including using alternative sensing technologies for blood oxygenation measures and improving the models’ specificity. This study represents a critical step in the integration of real-time sensing technologies for applications involving collective situation management and control.
... An automated medical triage system would need to be based on an integrated wearable platform. Recent technological improvements in wearable sensors and other human sensing technologies make it increasingly feasible to assess the human functional state in complex conditions [21]. Casualty detection, remote triage, and medical management are within the technological reach of integrated wearable platforms [22]. ...
The possibility to automate medical triage is appealing as it could decrease the burden on humans. In instances of mass casualty events, this could allow for much faster triage as the process would occur in parallel instead of in sequence. The greatest efficacy could be achieved with a system that relies solely on remote sensors, which would necessitate an adaptation of existing triage algorithms that rely on human observations. A policy capturing method is proposed to demonstrate the possibility to mimic medical experts’ decision-making model of triage based only on observable parameters sampled by wearables: heart rate, respiration rate, heart rate variability, and blood oxygen saturation. Two medical experts classified simulated cases from these five parameters and sex in regards of four potential outcomes: Delayed green, Urgent yellow, Immediate red, or Expectant black. Seven model types were trained to replicate the decision pattern of the experts. Overall, the decision pattern was best captured by a decision tree (test set accuracy of 92% and 76% for Raters 1 and 2, respectively). Interestingly, common physiological differences were found across the four classifications for both experts. The model was optimized during a workshop with the experts. We discuss the implications of using such a model to support medical triage, especially for military contexts.
... Part of that context can be implicitly detected through situation monitoring, but explicit information sharing about context and goals remains a key requirement for HMT in complex and dynamic environments [19]. User-aware systems that monitor in real-time operator state [20] or attentional focus [21] can also enable enhanced collaboration capabilities. Taking this notion even further, it has been proposed that mutual awareness is a prerequisite for adaptive human-AI collaboration [7]. ...
... During their two flights, participants were equipped with the Zephyr Bio Harness 3.0 chest strap, measuring the electrical activity of the heart, and respiration rate. The Sensor Hub [7] application was used on an android mobile phone to synchronize and process collected physiological data from the belt, as well as contextual data from the simulator (speed, altitude, global positioning system coordinates etc). ...
The training curriculum of air force cadets is currently identical for all, not taking trainees’ individual differences in skill acquisition into account. A model of physiological arousal conceptualized as “ease in flight” is proposed as an objective metric for individualization. Considering that a significant part of air force cadets training takes place on a flight simulator, the metrics used to provide cursus recommendation should be valid both in flight and in a simulator. This work concerns the validation of “ease in flight” as a metric for training individualization in a simulated task environment. Eight participants performed two consecutive flights on a low fidelity aircraft simulator, whilst wearing a chest strap to measure the electrical activity of the heart and respiratory activity. Results show that declared ease in flight and declared stress are strongly negatively correlated. In addition, measured ease in flight increased significantly from first to second flight. Together, these results suggest that the ease in flight model previously defined using data from experts in-flight generalizes to simulated flight, both from a perceived and objective point of view. Finally, the potential of the model for providing adaptive cursus recommendation through the individualized analysis of measured ease in flight across different required skills is discussed.
... In such cases, physiological measurements might be used as proxy for mental workload and performance, a technique used in previous research during flight simulation [7,8]. By using task independent metrics focused on bio-behavioural measurements, and by predicting changes in performance and workload, we aim to develop models usable in different training contexts, an approach previously used in different domains such as entertainment technologies [9], emergency management [10,11] and aerospace [7,8]. ...
The present work investigates a cumulative part-task training method that builds up task complexity adaptively based on individual learner states. A research-oriented game entitled “Space Fortress” was used to evaluate two training conditions in a between-group design prior to a third condition involving an adaptive cumulative part task training method. The latter detects when the learner is ready to progress and dynamically adjusts training progression. Here we report the results of the first two conditions. First was the full task condition, where the learner was exposed to the entire task throughout the training session. The second condition followed a cumulative part-task training approach, where sub-tasks were added at fixed progression points. Results showed no statistically significant gain nor loss in terms of learning outcomes between the full task and the non-adaptive cumulative part task condition, adding evidence to previous mixed findings. A trigger rule needed for the adaptive cumulative part task training condition was developed based on short-term patterns of change in performance and mental workload to be used as a dynamic criterion for adaptation. Furthermore, bio-behavioral measures were evaluated as potential proxies for performance and workload with the aim of applying this adaptive method in contexts where performance and workload cannot be directly measured at regular intervals.
... Similar systems have been proven efficient at predicting mental workload by providing relevant information on the state of operators, the situation and their environment in other high-risk contexts such as emergency management (cf. Gagnon, Lafond, Rivest, Couderc, & Tremblay, 2014). ...
Traffic workers are vulnerable to accidents and must make critical decisions to avoid conflicts between road users. This can lead to high stress levels, which may hinder their capacity to mitigate the occurrence of hazards. Measuring stress on the field could represent an efficient solution to help pinpoint risky situations and identify factors that increase risk. The goal of this study was to verify whether stress among traffic workers could be predicted using physiological measures and characteristics of the work situation. Nineteen police officers in Quebec City and Montreal, Canada, performed traffic duties while their physiological activity was assessed by a wearable physiological harness. Every 15 minutes, change in subjective stress was also measured. Results showed that decision-tree models outperformed multifactorial logistic regressions for predicting subjective stress based on both situational factors and physiological measures. This demonstrated the potential of using such measures to monitor stress among traffic workers.
... The rapid prototyping and implementation of custom interfaces is facilitated through the use of industry-standard software tools, while a number of custom-built, open-source and proprietary software is used to network the various environments within and external to the RMIT HFE-Lab. Similar to other research facilities at NASA [1][2][3], AFRL [4], THALES [5,6], DLR [7] and ENAC [8], research at RMIT HFE-Lab employs a virtual environment as a test-bed for evaluating novel and adaptive Human-Machine Interfaces and Interactions (HMI 2 ) concepts to support operations for the Next-Generation ATM network. ...
The simulation environment used in cognitive Human Factors Engineering (HFE) research at RMIT University HFE-Lab is presented in this article. The simulation environment consists of Air Traffic Management (ATM) workstations including Unmanned Aircraft System (UAS) Traffic Management (UTM) features as well as pilot/remote pilot stations, including an immersive research flight simulator. Additional modules are used in cognitive HFE research for collecting and processing psycho-physiological data, and for scenario management. An overview of the simulation environment, including the network, modules and tools is presented. An experimental case study involving eye tracking and cardiorespiratory measures is presented to demonstrate the capabilities of the HFE-Lab as a research tool for cognitive ergonomics and HFE research.
... The ability to monitor and adapt to the user's current attentional state may be particularly useful for keeping operators on task and goal-focused during the long shifts that are characteristic of CCTV surveillance. Such a research and development endeavourthat is, to develop intelligent systems that adapt their intervention to the cognitive and affective functional state of its useris long standing and requires measurements that capitalize on the online monitoring of multiple physiological sensors used as proxies to psychological dimensions (such as attention, stress, engagement and workload; see Durantin, Gagnon, Tremblay, & Dehais, 2014), and conceptually valid metrics capable of providing diagnostic information about the variability in the functional state of the operator (Gagnon, Lafond, Rivest, Couderc, & Tremblay, 2014). Promising results have identified physiological markers for attentional tunnelling (Dehais, Causse, & Tremblay, 2011) and the level of fun in video games (Chamberland, Grégoire, Michon, Gagnon, Jackson, & Tremblay, 2016), as well as methods to approximate mental models of decision makers (Lafond, Tremblay, & Banbury, 2013) that are the core of adaptive systems and individually-tailored cognitive assistants. ...
While the development of intelligent technologies in security surveillance can augment human capabilities, they do not replace the role of the operator entirely; as such, when developing surveillance support it is critical that limitations to the cognitive system are taken into account. The current article reviews the cognitive challenges associated with the task of a CCTV operator: visual search and cognitive/perceptual overload, attentional failures, vulnerability to distraction, and decision-making in a dynamically evolving environment. While not directly applied to surveillance issues, we suggest that the NSEEV (noticing – salience, effort, expectancy, value) model of attention could provide a useful theoretical basis for understanding the challenges faced in detection and monitoring tasks. Having identified cognitive limitations of the human operator, this review sets out a research agenda for further understanding the cognitive functioning related to surveillance, and highlights the need to consider the human element at the design stage when developing technological solutions to security surveillance.
The assessment of an operator’s functional state (i.e., the multidimensional pattern of human psychophysiological conditions that mediates performance) has great potential for increasing safety and reliability of critical systems. However, live monitoring of functional state using physiological and behavioral data still faces several challenges before achieving the level of precision required in many operational contexts. One open question is the level of granularity of the models. Is a general model sufficient or should subject-specific models be trained to ensure high accuracy? Another challenge concerns the formalization of a valid ground truth for training classifiers. This is critical in order to train models that are operationally relevant. This paper introduces the Decontextualized Dynamic Performance (DDP) metric which allows models to be trained simultaneously on different tasks using machine learning algorithms. This paper reports the performance of various classification algorithms at different levels of granularity. We compare a general model, task specific models, and subject-specific models. Results show that the classification methods do not lead to statistically different performance, and that the predictive accuracy of subject-specific and task-specific models was actually comparable to a general model. We also compared various time-window sizes for the new DDP metric and found that results were degrading with a larger time window size.
Task models can be very informative and provide invaluable guidance to the design of socio-technical artifact intended to provide external cognition and support to decision-making. In the case of developing support for strategic decision-making and complexity discovery, the modeling of task-related dynamic cognition is of prime importance. We report on the application of a method that combines hierarchical task analysis and process tracing - dynamic cognitive task modeling (DCTM) - as a means to inform the design process of a joint cognitive system (JCS) - IMAGE - that seeks to augment complexity discovery capabilities. The DCTM method provides a measure of human-system interaction and dynamic cognition that is empirical, unobtrusive, less sensitive to biases and well suited for the particularly challenging context of complexity discovery.
The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
Physiological computing involves the direct interfacing of human physiology and computer technology, i.e. brain–computer interaction
(BCI). The goal of physiological computing is to transform bioelectrical signals from the human nervous system into real-time
computer input in order to enhance and enrich the interactive experience. Physiological computing has tremendous potential
for interactive innovation but research activities are often disparate and uneven, and fail to reflect the multidisciplinary
nature of the topic. This paper will provide a primer on detectable human physiology as an input source, a summary of relevant
research and a research agenda to aid the future development of interactive systems that utilise physiological information.
We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.
This paper discusses how the fields of augmented cognition and neuroergonomics can be expanded into training. Several classification algorithms based upon EEG data and occular data are discussed in terms of their ability to classify operator state in real time. These indices have been shown to enhance operator performance within adaptive automation paradigms. Learning is different from performing a task that one is familiar with. According to cognitive load theory (CLT), learning is essentially the act of organizing information from working memory into long term memory. However, our working memory system has a bottleneck in this process, such that when training exceeds working memory capacity, learning is hindered. This paper discusses how CLT can be combined with multiple resource theory to create a model of adaptive training. This new paradigm hypothesizes that a system that can monitor working memory capacity in real time and adjust training difficulty can improve learning.
This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.
This paper describes an integrated Physiological Sensor Suite (PSS) based upon QUASAR's innovative non-invasive bioelectric sensor technologies that will provide, for the first time, a fully integrated, noninvasive methodology for physiological sensing. The PSS currently under development at QUASAR is a state-of-the-art multimodal array of sensors that, along with an ultra-low power personal area wireless network, form a comprehensive body-worn system for real-time monitoring of subject physiology and cognitive status. Applications of the PSS extend from monitoring of military personnel to long-term monitoring of patients diagnosed with cardiac or neurological conditions. Results for side-by-side comparisons between QUASAR's biosensor technology and conventional wet electrodes are presented. The signal fidelity for bioelectric measurements using QUASAR's biosensors is comparable to that for wet electrodes.
The last two decades have seen technological innovations that have revolutionized the collection and transfer of information, permitting access to and dissemination of massive amounts of data with unprecedented speed and efficiency. These innovations have been incorporated into virtually every aspect of modern society, from personal communications, to commercial and business processes, to governmental function and military operations. The concept of network-centric warfare (NCW) grew out of these new capabilities and has been a prominent topic in strategic and operational discussions in the US military since the late 1990s.In recent years, the concepts behind NCW have been increasingly applied to emergency response, particularly as responders prepare for an increasingly complex threat spectrum in a post-9/11 world. As emergency responders adopt the technological innovations and organizational concepts that enable network-centric operations, attention should be paid to the lessons learned by the US armed forces in the application of the network-centric approach to war-fighting. Emergency operations centers (EOCs), incident command centers (ICCs), and field personnel will require extensive training and experimentation to sort out the impact of this new technology. They must develop protocols and procedures to leverage maximum advantage, while avoiding the undesirable and damaging effects of that technology improperly applied. Because most emergency response organizations lack the vast training resources of the US military, they must be innovative and adaptable in taking advantage of every opportunity to train their personnel in the assimilation of this new technology.
Augmented cognition is a form of human-systems interaction in which a tight coupling between user and computer is achieved via physiological and neurophysiological sensing of a user's cognitive state. This interactive paradigm seeks to revolutionize the manner in which humans engage with computers by leveraging this knowledge of cognitive state to precisely adapt user-system interaction in real time. This review provides an overview of contemporary works in the field of augmented cognition and details regarding the three main components of an augmented cognition system: cognitive state sensors, adaptation strategies, and control systems. The review provides a perspective on the field as well as insights into the many challenges that lie ahead for those who endeavor to realize the full potential of augmented cognition.
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