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The papers in this special section focus on the development and applications supported by wearable robots. Wearable powered robots may be used for functional substitution in patients suffering from motor disorders, rehabilitation, assistance, and strength augmentation. Despite recent technological and scientific achievements, more research is needed to realize the promise of intuitive, easy-to-wear, safe, and effective wearable robots [ABSTRACT FROM AUTHOR]

The thirteen papers in this special section focus on the topic of movement science for humans and humanoids. The papers include the collection and organization of human movement data for enabling robotics research; the use of human movement as inspiration for humanoid planning, control, and motion generation; the development of algorithms for improved estimation of human and humanoid system parameters; and the use of human movement understanding in robotics applications including human-robot interaction and rehabilitation. [ABSTRACT FROM PUBLISHER]

The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies. [ABSTRACT FROM PUBLISHER]

As part of a drive to improve electrical safety in heavy industry, this paper presents a case history on an electrical arc flash accident. The accident occurred on a 3.3-kV-fused contactor which was locked out and tagged out. This paper describes the scenario leading up to the accident, the damage causing mechanisms, and concludes with certain root causes. It is envisaged that this paper will provide awareness on the importance of adequate management systems and the need to improve safety through design. [ABSTRACT FROM PUBLISHER]

This book comprises a selection of papers presented at the IPET 2008 symposium that took place at the Royal Academy of Engineering in London in November 2008 and at the following workshop at the Colorado School of Mines. It is the second volume in a series that emanated from a workshop on Philosophy in Engineering that took place at the University of Delft in October 2007 and that began a continuing series of workshops named by the acronym IPET followed by the year of the workshop. Prior to that, there had been three significant publications that had illustrated the value of pursuing the relationship between philosophy and engineering, and engineering education. The success of this 2007 University of Delft workshop led to IPET 2008 being held at the Royal Academy of Engineering in London. While the focus of both volumes is on engineering, necessarily many of the papers have implications for engineering education. It would be surprising if the three individuals invited to review this book were able to agree upon a conclusion. They did indeed have different views; this seems to be because their views differed of who was likely to read this book. A reader may infer the editors? intentions from the questions put to Mitcham, but it is harder to infer the intended audience. One of the reviewers takes the view that a collection of papers of the calibre presented will attract a like-minded set of readers, and so responded to the questions put to Mitcham with that group in mind. Another thought the book would be suited to engineering educators more generally, and as a result was effusive in its praise. The fundamental questions that need to be answered are ?What value do these papers have for engineering educators?? and ?What value do they have for their students?? While the editors have not focused on engineering and technological literacy, a number of chapters are of considerable importance to those who promote study in these areas. The chapters on peace engineering and the use of robotics in warfare are examples of this. Of the series of papers on the philosophy of technology adoption over the last two decades, some are summarized in Ch. 23 by Houkes and Pols. This chapter focuses on the designer?user system in terms of what the authors' call Rational Acceptance Technology (RAT), one aspect of which is control. They point out that in designing artefacts, designers hand over a measure of control to the users. RAT requires that the user has partial responsibility for failure. For instance, guarantees and insurance for domestic electrical goods are careful to note improper use as an exclusion from the guarantee. From the reviewers' perspective, the answer to the first of the questions put to Mitcham is that this volume contributes to the establishment of a philosophy of engineering. It now requires an authority to provide a synthesis of the numerous articles that have been published in this area with that in mind. A good starting point would be for the organizers of these and similar workshops to call for some focused review papers, to which the reviewers and readers of this book can look forward to with great anticipation. [ABSTRACT FROM PUBLISHER]

Despite recent advances in aircraft maintenance technology, the breakdowns and catastrophic accidents due to human failure during maintenance activities will continue to occur. This paper argues that the impact of human error is amplified by the increased frequency of activities as a result of premature maintenance (PRM). PRM refers to predated or early maintenance tasks resulting in additional activities over a long horizon. A novel methodology is proposed to monetize the impact of PRM and show its contribution to total maintenance cost. The proposed methodology is inspired by the proportional hazard model to capture the effects of human failure on the system’s hazard rate. Our findings show that the contribution of PRM to the total operating cost is noticeable; this justifies a level of flight delay or even cancellation due to accommodate on-time maintenance activities. [ABSTRACT FROM AUTHOR]

Lean leadership might be among the most desirable goals for the development of successful managers. The most prominent organization exhibiting this style is Toyota. This article provides a comprehensive examination of leadership styles and their relation to the two lean principles, respect for people, and continuous improvement. The results shed light on critical leadership development for lean initiatives. Implications for theory and practice are discussed, and suggestions for future research are proposed. Managerial Relevance Statement-Lean management creates organizations which are financially sound and ethical. Major obstacles to create such lean management driven organizations seem to be soft factors of human behavior driven by leadership. This paper undertakes an effort to analyze the desirable leadership style(s) to enable managers and leaders to sustain or introduce lean management in their organization effectively. [ABSTRACT FROM AUTHOR]

Stress is one of the contributing factors affecting decision-making. Therefore, early stress recognition is essential to improve clinicians’ decision-making performance. Functional near-infrared spectroscopy (fNIRS) has shown great potential in detecting stress. However, the majority of previous studies only used fNIRS features at the individual level for classification without considering the correlations among channels corresponding to the brain, which may provide distinguishing features. Hence, this study proposes a novel joint-channel-connectivity-based feature selection and classification algorithm for fNIRS to detect stress in decision-making. Specifically, this approach integrates feature selection and classifier modeling into a sparse model, where intra- and inter-channel regularizers are designed to explore potential correlations among channels to obtain discriminating features. In this paper, we simulated the decision-making of medical students under stress through the Trier Social Stress Test and the Balloon Analog Risk Task and recorded their cerebral hemodynamic alterations by fNIRS device. Experimental results illustrated that our method with the accuracy of 0.961 is superior to other machine learning methods. Additionally, the stress correlation and connectivity of brain regions calculated by feature selection have been confirmed in previous studies, which validates the effectiveness of our method and helps optimize the channel settings of fNIRS. This work was the first attempt to utilize a sparse model that simultaneously considers the sparsity of features and the correlation of brain regions for stress detection and obtained an admirable classification performance. Thus, the proposed model might be a useful tool for medical personnel to automatically detect stress in clinical decision-making situations. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain challenging due to its variability. In this paper, a novel deep neural network combining convolutional neural network (CNN) and adversarial theory, named symmetric deep convolutional adversarial network (SDCAN), is proposed for stress classification based on EEG. The adversarial inference is introduced to automatically capture invariant and discriminative features from raw EEG, which aims to improve the classification accuracy and generalization ability across subjects. Experiments were conducted with 22 human subjects, where each participant’s stress was induced by the Trier Social Stress Test paradigm while EEG was collected. Stress states were then calibrated into four or five stages according to the changing trend of salivary cortisol concentration. The results show that the proposed network achieves improved accuracies of 87.62% and 81.45% on the classification of four and five stages, respectively, compared to conventional CNN methods. Euclidean space data alignment approach (EA) was applied and the improved generalization ability of EA-SDCAN across subjects was also validated via the leave-one-subject-out-cross-validation, with the accuracies of four and five stages being 60.52% and 48.17%, respectively. These findings indicate that the proposed SDCAN network is more feasible and effective for classifying the stages of mental stress based on EEG compared with other conventional methods. [ABSTRACT FROM AUTHOR]

Scenario-based proving ground testing has been established as the standard test environment for the homologation of driver assistance systems. These test procedures are described, for example, in the New Car Assessment Programme test catalog, which only considers a limited number of possible scenarios. Furthermore, pedestrian targets are, among others, used to test assistance systems for protection against vulnerable road users (VRUs). However, these targets also have the disadvantage that they only represent humans in a simplified way and cannot depict realistic movement patterns. On the other hand, simulation has been established as an efficient means of realizing scenarios in a reproducible, time- and cost-saving manner. However, simulation has a major disadvantage: it can only represent reality to a limited extent, e.g., in the influence of human factors in response to hazardous situations. To bridge the gap between simulation and real-world testing, we introduce MiRE, a mixed reality environment for testing automated driving functions. In MiRE, real entities meet in a virtual environment and take advantage of real and simulated test procedures. This paper presents MiRE, a proof of concept based on a modular architecture. The goal is to use it to evaluate the feasibility of testing in mixed reality – by comparing it to proving ground testing. Preliminary results show that MiRE is a suitable environment for testing complex scenarios where the real vehicle and VRUs can safely interact in safety-critical situations and produce realistic results. [ABSTRACT FROM AUTHOR]

Text summarization techniques have been found to be effective with regard to helping users find relevant information faster. The effectiveness and efficiency of a user's performance in an information-seeking task can greatly be improved if he/she needs to only look at a summary that includes the relevant information presented in his/her preferred manner. On the other hand, if the main idea is misrepresented and/or omitted altogether from a summary, it may take users more time to solve a target problem or, even worse, lead users to make incorrect decisions. There is an important need to design a personalized text summarization system that takes into account both what a user is currently interested in and how a user perceives information. The latter factor is referred to as a user's cognitive styles. Although there are some existing approaches that have employed a user's interests to help in the design of a personalized text summarization system, there has been inadequate focus on exploring cognitive styles. This paper aims at studying the impact of a user's cognitive styles when assessing multidocument summaries. In particular, we choose two dimensions of a user's cognitive style—the analytic/wholist and verbal/imagery dimensions—and study their impacts on how a user assesses a summary that was generated from a set of documents. In particular, the type of a document set refers to whether the set's content is loosely or closely related. We use a document set type to explore if there are any differences in the users' assessments of summaries that were generated from sets of different types. The results of this paper show that different users have different assessments with regard to information coverage and the way that information is presented in both loosely and closely related document sets. In addition, we found that the coherency ratings that were given to summaries from the two types of document sets were significantly different between the analytic and wholist groups. This result leads us to investigate the impact of a user's cognitive styles and the following two factors that directly relate to the coherence of a summary: 1) graph entropy and 2) the percentage of stand-alone concepts. We found that these two factors and a user's cognitive styles affect a user's ratings on the coherency of a summary. [ABSTRACT FROM PUBLISHER]

Despite the advances related to car-following and lane-changing behaviors, the influence of lane-changing on the car-following models, which results in a complex transient merging behavior, has not comprehensively been investigated. This paper presents a novel fuzzy controller based on a human factor to optimize the Follower Vehicle (FV) behavior subject to safety, comfort, and convenient traveled time in the complex behavior where the Lane Changer (LC) vehicle exits the temporary lane. The factor enables the controller to mimic the current driver behavior in terms of maximum pleasantness of drive. Accordingly, the data of real-life experiments were used to design the human-like fuzzy controller, to build a predictive model to suggest the appropriate acceleration, velocity, and travel distance. At best, the correlation coefficient of 0.93 and the Root Mean Square Error (RMSE) of 0.71 were achieved for modeling using the adaptive Neuro-Fuzzy Inference System (ANFIS) utilizing Gaussian function as a membership function. Furthermore, to evaluate the robustness of the controller to uncertainties and unknown disturbances for real-time driving experiments, a test-bed was fabricated to mount the feedback sensors, including vision, accelerometer, and distance measurement sensors. The results of running the controller in various driving scenarios showed 70% and 38% improvements in safety and ride comfort, respectively. The proposed intelligent controller is intended to be used for vehicle route guidance and on urban highways. [ABSTRACT FROM AUTHOR]

Human factors, such as an individual's competences and attitudes, have a decisive impact on the results of product development processes, especially in companies with small product development teams. Sustainability considerations further amplify this impact as such a multifaceted issue results in an extra layer of product requirements and hard-to-make decisions on tradeoffs. This paper explores the interplay of corporate sustainability and the individual approaches product developers exhibit toward improving product sustainability. For this purpose, a grounded theory study in the German consumer goods industry is conducted. Thirty-two expert interviews with product development managers and extensive secondary data are collected and analyzed. It is found that the corporate sustainability approach heavily influences how developers comprehend sustainability and how they conceptualize it for their product portfolio. Explicitly, the products considered for sustainability improvements, their innovation level, and the use of design stereotypes to signal sustainability improvements emerge as key decision levers. The findings emphasize that the human factors in the context of product development, specifically concerning sustainability, warrant more academic attention. Also, it is demonstrated that companies need to be aware of the organizational environment which they are providing for their developers when pushing for product sustainability. [ABSTRACT FROM AUTHOR]

As demand for air transport continues to grow, the complexity of the operating environment of air traffic has intensified. Air Traffic Controllers (ATCos) are faced with the critical task of making timely decisions in response to rapidly changing air traffic. In this context, maintaining Situation Awareness (SA) becomes critical, directly influencing ATCos’ decision making and preventing potential traffic accidents or incidents. This paper presents a systematic review of the theoretical development of SA measurement techniques for ATCos. Firstly, the measurement techniques developed to assess individual and team SA are discussed and summarized. Additionally, some recently developed novel measurement methods are introduced. Four specific techniques applied to evaluate ATCos’ SA are highlighted. Second, the article analyzes the comprehensive utilization of neurophysiological measurement techniques, with a particular focus on eye movement and EEG methodologies. These techniques exhibit promising potential in measuring ATCos’ SA and are explored in-depth. Furthermore, four distinct types of sensitivity factors that primarily affect ATCos’ SA are summarized. This paper provides some insight into the current state of SA measurement techniques for ATCos. It concludes with recommendations for future research, specifically addressing the evaluation of ATCos SA in high-automation environments.

In this paper, we study the impact of human mobility on throughput and delay for people-centric applications in mobile ad hoc networks (MANETs). We consider a general human mobility model for MANETs, which can capture important features of human mobility, such as time correlation, node correlation, location heterogeneity, and node heterogeneity. Multiple unicasts with general arrival processes are delivered, and nodes are equipped with infinite buffers. Under our system model, we first characterize the network stability region in terms of the probability of each node set visiting each location and the amount of transmission resources at each location. We show that the node correlation and heterogeneity of locations' popularity usually decrease the size of the network stability region, whereas the diversity of locations visited by a node usually increases the size of the network stability region. Then, by solving a stability-related optimization problem, we develop a throughput-optimal policy based on the obtained optimal solution. We obtain the upper and lower bounds of the delay performance under the proposed policy. Finally, using simulations based on a theoretical model and some real traces, we verify the analytical results and compare the performance of the proposed policy with some existing policies. [ABSTRACT FROM PUBLISHER]

Every year 2.3 million people die worldwide due to occupational illnesses and accidents at work. By analyzing the workers’ behavior when in the presence of risks managers could assign tasks to those workers who appear to be the most sensitive to risk being assigned and are thus more likely to exert more caution in the presence of that risk. This paper presents a novel multiobjective formulation of the personnel assignment problem, maximizing workers’ sensitivity to risk, while minimizing cost and dislike for the task assigned. A worker’s sensitivity to risk for a task is quantified by a new measure, carefulness, which stems from the worker’s behavior and various human factors that affect the interaction with the risk. The problem is solved using a mixed evolutionary and multicriteria decision making methodology. An approximation of the Pareto front is first generated through the nondominated sorting genetic algorithm II. A hybrid decisional approach then exploits the technique for order of preference by similarity to ideal solution in order to select the Pareto-optimal solution that represents the nearest compromise to the decision-maker’s preferences. These preferences are derived through a fuzzy version of the analytic hierarchy process. The proposed framework was tested in four real-world scenarios related to manufacturing companies. The results show a significant increase in overall carefulness and a strong decrease in the dislike for the task assigned, with a modest increase in cost. The framework thus improves the work climate and reduces the risk occurrence and/or the impact on the workers’ health. [ABSTRACT FROM AUTHOR]

Research on modelling user quality of experience (QoE) to date has primarily focused the combination of traditional media components; audio and video, or the individual influence of each. However, multisensory experiences have recently gained significant traction in the research community as a novel method to enhance QoE beyond what is possible with traditional media. This paper presents a model developed based on empirical data. It estimates user QoE of olfaction-enhanced multimedia. A set of 12 olfaction enhanced video clips were viewed by 84 assessors. A strong age and gender balance produced 6048 user ratings across six questions. Employing this dataset, the proposed model considers the influence of: system factors, user factors and content factors on user perceived QoE. The model is instantiated and validated. The analysis indicates that: content factors have a 10% influence on user QoE; age factors have an 11% influence; and gender factors have an 8% influence on user QoE. Also, content factors had the highest number of statistically significant influences across all of the factors evaluated. These results suggest that human, and content in addition to system factors play a key role in perceptual multimedia quality of olfaction enhanced multimedia. Further work is required to understand the remaining factors as well as the relationship between the media components has on QoE. [ABSTRACT FROM AUTHOR]

This paper proposes a novel pilot control model which reflects the following three characteristics of the pilot control behavior: stochastic, periodical, and discrete movement. The focus is on the final descent phase when the pilot controls the aircraft manually based on the flight director commands. The proposed model is developed based on an existing model as well as highly experienced pilot's comments. A flight simulator experiment is conducted and three pilots’ landing data are obtained. The parameters of the pilot model are tuned via a genetic algorithm. The simulation result reveals that the proposed model captures well the characteristics of the data obtained in the simulator experiment and shows a good accordance with actual command tracking capability. The obtained parameters also identify the difference of control strategies between the pilots. [ABSTRACT FROM PUBLISHER]

In the context of the hierarchy of risk controls, warnings, administrative controls, and personal protective equipment are sometimes referred to as lower order controls. In practice, the effectiveness of these controls can be overestimated due to a misunderstanding of the human factors that affect error likelihood in their implementation. This paper explores one human factor: the limitation of attention on residual risk inherent in lower order controls. The limitation of attention has implications for identifying hazards, assessing risk, taking appropriate actions based on warnings, executing safe work practices, and proper selection and use of personal protective equipment. Raising awareness and improved understanding of these topics can enable more effective risk assessment, quantification of residual risk, and more effective selection and combination of risk controls to achieve risk as low as reasonably practicable. [ABSTRACT FROM PUBLISHER]

This paper focuses on graphic-based approaches and proposes a fuzzy knowledge-based disassembly process planning (FDPP) system for DPP. This FDPP system is based on a novel high-level Petri net (PN), termed fuzzy attributed and timed predicate/transition net (FATP/T net). An FATP/T net model for DPP is developed and the model is found very compact and general to any products, due to the comprehensive modeling capability and the inclusion of a bill of material component. It avoids issues such as unmanageable big model size and specific to a product, which are often found in the DPP models based on ordinary PNs. Another feature of the FATP/T net model is that it also includes a functional test operation to discern the usability of detached subassemblies/components. In addition, we have proposed a systemic approach to transform the FATP/T net model into an FDPP system to support DPP at a system level. A telephone kit is used as an example to illustrate the applicability of the FDPP system. The FDPP system is found flexible and expandable due to the independence of its components. [ABSTRACT FROM PUBLISHER]

As people increase their dependency on mobile applications and services, saving power consumption of mobile devices becomes an important challenge for supporting video streaming applications. This paper investigates how to minimize the power consumption of an organic light-emitting diode (OLED) display when displaying a video under consideration for the user’s visual experience. We first model the minimization problem as an OLED video scaling optimization problem. We then propose an algorithm to solve the optimization problem and prove that the algorithm is optimal in terms of power savings. We next introduce an energy-saving cloud service based on the algorithm. Finally, the results of the extensive experiments conducted on a commercial mobile smartphone with four real-world videos to evaluate the performance of the proposed algorithm are very encouraging. [ABSTRACT FROM AUTHOR]

While the growing number of learning resources increases the choice for learners on how, what and when to learn, it also makes it more and more difficult to find the learning resources that best match the learners' preferences and needs. The same applies to learning systems that aim to adapt or recommend suitable courses and learning resources according to a learner's wishes and requirements. Improved representations for a learner's preferences as well as improved search capabilities that take these preferences into account leverage these issues. In this paper, we propose an approach for selecting optimal learning resources based on preference-enabled queries. A preference-enabled query does not only allow for hard constraints (like 'return lectures about Mathematics') but also for soft constraints (such as 'I prefer a course on Monday, but Tuesday is also fine') and therefore allow for a more fine-grained representation of a learner's requirements, interests and wishes. We show how to exploit the representation of learner's wishes and interests with preferences and how to use preferences in order to find optimal learning resources. We present the personal preference search service~(PPSS), which offers significantly enhanced search capabilities for learning resources by taking the learner's detailed preferences into account. [ABSTRACT FROM PUBLISHER]

Disassembly, as the process of systematic removal of desirable constituent parts from an assembly, is of growing importance due to the increasing environmental and economic pressure. Although disassembly in practice is manual and labor intensive, little attention has been paid to the human intervention in the disassembly process. This paper addresses this deficiency by developing a fuzzy attributed Petri net (FAPN) model to mathematically represent uncertainty in disassembly due to a large amount of human intervention. An algorithm based upon this model is further proposed for optimal disassembly planning with a view to making the technique more applicable to real industry settings. The benefit of the proposed model and algorithm is illustrated through the disassembly of a personal computer (PC) in a prototypical disassembly system. [ABSTRACT FROM AUTHOR]

How to build a sustainable society in view of industrial electronics has been discussed from energy, information and communication technologies, cyber-physical systems (CPSs), and other viewpoints. This paper presents a cross-disciplinary view that integrates the fields of human factors, professional education, electronic systems on chip, resilience and security for industrial applications, technology ethics and society, and standards. After explaining the efforts and challenges in these fields, this paper shows a methodology for cross-disciplinary technology that integrates the technical committees in Cluster 4, Industrial Electronics Society. A project, which was launched in March 2022, implements a ‘Proof of Concept’ trial of the methodology.

Critical success factors dictate that query languages be 1) easy to learn, 2) easy to understand, and 3) easy to use. Although a query language may influence the user's behavior by its syntax, it is unrealistic to evaluate a query language without considering the influence of its interactive user interface features. In addition, in order to allow for a fair comparison of query languages, the languages should be 1) directed toward the same type of users, 2) used in a similar system environment, and 3) use the same DBMS. This research focuses its attention on comparing SQL and QBE in the same operating environment and studying the effects of query language type and other variables on user performance and satisfaction. The experimental design combined a factorial design and a counterbalanced design in an effort to compare SQL and QBE. The results indicated that query language type affects user performance in paper and pencil testing with QBE users having higher scores than SQL users. In contrast, in on-line testing, query language type had no effect on user performance. In addition, under certain conditions, query complexity had a significant effect on user performance and user satisfaction was influenced by query language type. Moreover, order of exposure impacted user performance on the basis of its interaction with query language type, query complexity, and programming experience. [ABSTRACT FROM AUTHOR]

Human operators play a key role in the reliable operation of critical infrastructures. However, human operators may take actions that are far from optimum. This can be due to various factors affecting the operators’ performance in time-sensitive and critical situations such as reacting to contingencies with significant monetary and social impacts. In this paper, an analytic framework is proposed based on Markov chains for modeling the dynamics of cascading failures in power grids. The model captures the effects of operators’ behavior quantified by the probability of human error under various circumstances. In particular, the observations from historical data and information obtained from interviews with power-system operators are utilized to develop the model as well as identify its parameters. In light of the proposed model, the noncritical regions of power-system's operating characteristics with human-factor considerations are characterized under which the probability of large cascading failures is minimized. [ABSTRACT FROM AUTHOR]

The articles in this special section focus on how disruptions whether they be natural or human impact society, technological development, and future outlooks. This section explores the opportunities and challenges of leveraging AI and other digital capabilities to drive the transformative system changes needed to tackle the climate crisis and related global sustainability challenges. The work presented here was initiated well before the pandemic emerged, but the current crisis makes the issues explored in these papers even more poignant for challenges we face in building the world we want. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel scheme for human action recognition that combines the advantages of both local and global representations. We explore human silhouettes for human action representation by taking into account the correlation between sequential poses in an action. A modified bag-of-words model, named bag of correlated poses, is introduced to encode temporally local features of actions. To utilize the property of visual word ambiguity, we adopt the soft assignment strategy to reduce the dimensionality of our model and circumvent the penalty of computational complexity and quantization error. To compensate for the loss of structural information, we propose an extended motion template, i.e., extensions of the motion history image, to capture the holistic structural features. The proposed scheme takes advantages of local and global features and, therefore, provides a discriminative representation for human actions. Experimental results prove the viability of the complimentary properties of two descriptors and the proposed approach outperforms the state-of-the-art methods on the IXMAS action recognition dataset. [ABSTRACT FROM AUTHOR]

Effective display power management is essential to reduce the cost of operating desktop computers as well as prolonging the lifetime of battery-operated notebooks and laptops between consecutive battery recharges. Although displays are dedicated for our eyes, existing display power management technologies “sense” only our fingers or temperature or motion through keyboard, mouse, or special sensors. Therefore, display may either switch off when its user looks at the screen or loose energy by keeping the screen on when nobody looks at it. This paper discusses a new display power management technology and outlines its implementation in personal computer systems. Unlike presently used power management, our technology “watches” the user through a single video camera. The technology tracks the user's eyes, keeping the display active only if the user looks at the screen. Otherwise, it dims the display down or even switches it off to save energy. We implemented the technology in field programmable gate array-based hardware and present results of its experimental evaluation. [ABSTRACT FROM AUTHOR]

A control-theoretic model of the bicycle rider is developed. The model has its origins in pilot modeling efforts previously reported in the literature. A handling-quality metric that was employed in pilot/vehicle analysis is adopted for use in estimating the task-independent handling qualities of bicycles. The resulting model is parameterized by five gains, two fixed second-order filters, and a preview time. Analysis and computer simulation of the rider/bicycle system are undertaken using six linear models of existing bicycles at three different velocities. The rider's task consisted of a 2-m lane-change maneuver and return. Lane tracking performance was comparable for all bicycles at each velocity. Distinct variations in estimated handling-quality levels were evident in the analysis that indicated that bicycle velocities, rather than differences in the bicycles themselves, dominated the handling-quality predictions. A brief discussion of a rider control model for hands-free riding and a possible approach for model identification concludes this paper. [ABSTRACT FROM PUBLISHER]

Building information modeling is only beginning to incorporate human factors, although buildings are sites where humans and technologies interact with globally significant consequences. Some buildings fail to perform as their designers intended, in part because users do not or cannot properly operate the building, and some occupants behave differently than designers expect. Innovative buildings, e.g., green buildings, are particularly susceptible to usability problems. This paper presents a framework for prospectively measuring the usability of designs before buildings are constructed, while there is still time to improve the design. The framework, which was implemented as an agent-based computer simulation model, tests how well buildings are likely to perform, given realistic occupants. An illustrative model for lighting design shows that this modeling approach has practical efficacy, demonstrating that, to the extent that users exhibit heterogeneous behaviors and preferences, designs that allow greater local control and ease of operation perform better. [ABSTRACT FROM PUBLISHER]

Recently, much research in the area of haptic technologies has focused on the development of waist-worn vibrotactile belts as substitution or augmentation modalities for audio-visual information. Vibrotactile belts have been used in varied applications, such as navigational aids, spatial orientation display, and balance control. Researchers have mostly focused on the functionality of these vibrotactile belts for specific applications while neglecting performance and usability. Considering the versatility of a vibrotactile belt, we previously conducted a study on the design requirements for vibrotactile belts and introduced an implementation based on these design guidelines. This paper builds on our previous work and provides details for the implementation of a ubiquitous wearable vibrotactile belt. A case study is presented in which the proposed belt was used by a researcher for a novel application of teaching participants choreographed dance. The usability of the belt is demonstrated from the researcher's perspective in terms of functionality and performance and from the participants' perspectives in terms of usability attributes such as comfort and unobtrusiveness. [ABSTRACT FROM AUTHOR]

One of the most important issues in road safety management is the lack of reliable methods for predicting the likelihood of accidents. Road safety assessment systems have been developed; however, these systems only employ historical or retrospective analyses, and the human factor element is weak or missing. Effective safety management requires both holistic and prospective viewpoints, with human factors having an intrinsic role. The main goal of this paper is to contribute toward that need through the application of Bayesian belief networks and road traffic simulation for validating the safety requirements of prospective road designs. The theoretical platform of the method is the concepts of human performance and mental workload and how these affect accident likelihood. This paper presents a novel method and a tool that integrates these two mature technologies, for assessing the safety performance of road designs before they are developed. A case study is included that illustrates the application of the method and tool. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel laser-based wide-area heads-up windshield display which is capable of actively interfacing with a human as part of a driver assistance system. The dynamic active display (DAD) is a unique prototype interface that presents safety-critical visual icons to the driver in a manner that minimizes the deviation of his or her gaze direction without adding to unnecessary visual clutter. As part of an automotive safety system, the DAD presents alerts in the field of view of the driver only if necessary, which is based upon the state and pose of the driver, vehicle, and environment. This paper examines the effectiveness of DAD through a comprehensive comparative experimental evaluation of a speed compliance driver assistance system, which is implemented on a vehicular test bed. Three different types of display protocols for assisting a driver to comply with speed limits are tested on actual roadways, and these are compared with a conventional dashboard display. Given the inclination, drivers who are given an overspeed warning alert reduced the time required to slow down to the speed limit by 38% (p < 0.01) as compared with the drivers not given the alert. Additionally, certain alerts decreased distraction levels by reducing the time spent looking away from the road by 63% (p < 0.01). Ultimately, these alerts demonstrate the utility and promise of the DAD system. [ABSTRACT FROM AUTHOR]

This paper presents experimental results associated with human factor aspects of using animation to display electric power system flow information, including transmission line megawatt flow and power transfer distribution factor (PTDF) values. The paper's results are based on two experiments performed at the University of Illinois at Urbana-Champaign using electric power system students. The results indicate that animated motion of power system flows can be used successfully in displays to improve both the speed and accuracy of certain tasks. This effect was most apparent on displays showing PTDFs. However, the results also show that motion may not provide a clear advantage in the visualization of transmission line flows for uncomplicated analysis tasks. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major societal challenge. By advising drivers on the optimal lane to drive, traffic flow can be improved, and congestion reduced. In this paper we describe the development of a lane-specific advice Human Machine Interface (HMI). Persuading drivers to follow an advice that is beneficial to the traffic situation, but may not be immediately beneficial to the drivers themselves, is challenging. In this paper we define persuasive elements to encourage drivers to follow the lane-specific advices. We then describe the interface design process, followed by its evaluation using a driving simulator study. In the simulator study, the effect of two types of persuasion are evaluated: a competitive variant where drivers could earn points and compete with others, and a cooperative variant where real-time information on the number of compliant drivers was available. Participants drove in the simulator on two days. Between days, the treatment groups viewed a Web-portal showing their performance and encouragement from an avatar. Those in the competitive and cooperative groups followed significantly more advices (117 and 111) than those in the control group (89). No significant differences were visible between competitive and cooperative groups. The differences between groups only emerged on the second day.

Higher education institutions (HEI) have realized the advantages of implementing a blended learning environment by using learning management systems (LMSs) to provide effective ways of learning and teaching. However, recent literature has revealed that developing countries' educational institutions fail when they adopt LMS technologies because they ignore the human factors related to these technologies. This study investigated the impact of human factors on the effectiveness of LMS in a blended learning environment at Kuwait's HEIs (KHEIs). In this paper, a pragmatic approach consisting of quantitative and qualitative methods is used. User characteristics traits from the literature review were organized into three main indicators of human impact on the effectiveness of LMS: technological characteristics, psychological characteristics, and student-instructor interaction characteristics. After these indicators were identified, a conceptual model was developed to study the characteristics. A deductive approach was used, and the model was tested for significance through receiving feedback from the universities' students and faculty members. In addition, an inductive approach was implemented through conducting interviews to explore more factors related to the effectiveness of LMS. The inductive approach's results revealed the relevant factors for the usage of the LMS, such as training, needs, expectations, and branding. The human factors that positively affect the success and effectiveness of LMS included attitude, enjoyment, experience, self-efficacy, and promptness, which had the most substantial impacts on user satisfaction. The modified conceptual model that this paper presents includes these impacting factors and provides an acceptable fit to the data. This research has developed an updated framework for demonstrating the effectiveness of LMS as it pertains to human factors, and its findings can be used to enhance user experience in a blended learning environment, thus improving the quality of Kuwaiti education.

Formal grammatical description has not generally been applied in the human factors area, which traditionally draws on behavioral science for its methodology. This paper illustrates, by means of a detailed example, how formal grammatical description can be used as a predictive tool to compare alternative designs for ease of use and to identify design choices which could cause users to make mistakes. The paper describes the human interface for two versions of an interactive graphics system intended for use by nonprogrammers. It presents the "action languages" for the two versions, then shows how these user languages can be described in terms of a production rule notation. Particular emphasis is given, in the notation, to actions the user has to learn and remember (i.e., to "cognitive" factors). The paper then presents predictions about human performance based on the formal description, and exploratory results of testing some of the predictions. Since the predictions arc based on general properties of the formal description, the technique should also be applicable to other "action languages." [ABSTRACT FROM AUTHOR]

Planning surgical interventions is a complex task, demanding a high degree of perceptual, cognitive, and sensorimotor skills to reduce intra- and post-operative complications. This process requires spatial reasoning to coordinate between the preoperatively acquired medical images and patient reference frames. In the case of neurosurgical interventions, traditional approaches to planning tend to focus on providing a means for visualizing medical images, but rarely support transformation between different spatial reference frames. Thus, surgeons often rely on their previous experience and intuition as their sole guide is to perform mental transformation. In case of junior residents, this may lead to longer operation times or increased chance of error under additional cognitive demands. In this paper, we introduce a mixed augmented-/virtual-reality system to facilitate training for planning a common neurosurgical procedure, brain tumour resection. The proposed system is designed and evaluated with human factors explicitly in mind, alleviating the difficulty of mental transformation. Our results indicate that, compared to conventional planning environments, the proposed system greatly improves the nonclinicians’ performance, independent of the sensorimotor tasks performed ($p\,<\,0.01$ ). Furthermore, the use of the proposed system by clinicians resulted in a significant reduction in time to perform clinically relevant tasks ($p\,<\,0.05$ ). These results demonstrate the role of mixed-reality systems in assisting residents to develop necessary spatial reasoning skills needed for planning brain tumour resection, improving patient outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in anon-euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance tomeasure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Data-centric artificial intelligence (AI) (DCAI) has the potential to bring significant benefits to society; however, it also poses significant challenges and potential risks. It is crucial to approach the development and deployment of DCAI systems with caution, taking into account the potential societal impacts and working to mitigate any negative effects. DCAI technology is now an essential part of operations for many of the world’s largest software and hardware industries. These industries offer a range of AI and machine-learning (ML) services, tools, and platforms to society to help businesses process and analyze data. By leveraging data, these industries are able to drive innovation, optimize their operations, and gain a competitive advantage in the market. From personalized recommendations to optimized manufacturing processes, data analytics and ML algorithms are being used to improve the overall customer experience, increase efficiency, and identify new opportunities for growth. As data continue to play an increasingly important role in business operations, it is likely that more companies will adopt these technologies to stay ahead of the curve and succeed in today’s data-driven world , ,. [ABSTRACT FROM AUTHOR]

Technology helps to solve problems, but it may also lead to unintended consequences. For example, biofuels may help to overcome the disadvantages of fossil fuels, but their production might compete with food production leading to higher food prices and hunger. Therefore, in recent decades, the societal impact of technology has come to the center of attention. To deal with potential ethical issues related to technology, many scholars have emphasized the importance of addressing values already during the design phase of new technology. Values are understood as beliefs about what is good or desirable, like human autonomy, safety, sustainability, or privacy. Researchers in ethics and philosophy of technology have developed a variety of approaches, like value-sensitive design (VSD) and responsible research and innovation, to help engineers embed values in technological design (e.g.,). [ABSTRACT FROM AUTHOR]

Wristband-placed physical activity monitors, as a convenient means for counting walking steps, assessing movement, and estimating energy expenditure, are widely used in daily life. There are many consumer-based wristband monitors on the market, but there is not an unified method to compare their performance. In this paper, we designed a series of experiments testing step counting performance under different walking conditions to evaluate these wristband activity monitors. Seven popular brands, including Huawei B1, Mi Band, Fitbit Charge, Polar Loop, Garmin Vivofit2, Misfit Shine, and Jawbone Up, were selected and evaluated with the proposed experiment method in this paper. These experiments include four parts, which are walking in a field at a different walking speed with and without arm swing, walking along a specified complex path, walking on a treadmill, and walking up and down stairs. Experiment results and analysis with nine healthy subjects were reported to show the step counting performance of these seven monitors.

The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). A multidimension framework and a BI methodology-based ontological modeling approach have been developed to implement a Data-Brain. The Data-Brain, Data-Brain-based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain-based brain data center which provides a global framework to integrate data, information, and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms. [ABSTRACT FROM PUBLISHER]

Nowadays, among the microscopic traffic flow modeling approaches, the car-following models are increasingly used by transportation experts to utilize appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, in this paper, a modified neural network approach is proposed to simulate and predict the car-following behavior based on the instantaneous reaction delay of the driver–vehicle unit as the human effects. This reaction delay is calculated based on a proposed idea, and the model is developed based on this feature as an input. In this modeling, the inputs and outputs are chosen with respect to the reaction delay to train the neural network model. Using the field data, the performance of the model is calculated and compared with the responses of some existing neural network car-following models. Considering the difference between the responses of the actual plant and the predicted model as the error, comparison shows that the error in the proposed model is significantly smaller than that that in the other models. [ABSTRACT FROM PUBLISHER]

Powered wheelchair users often struggle to drive safely and effectively and, in more critical cases, can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists users as and when they require help. The system uses a multiple-hypothesis method to predict the driver's intentions and, if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance but also, perhaps more importantly, characterize the user performance in an experiment that combines eye tracking with a secondary task. Without assistance, participants experienced multiple collisions while driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely but also they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain–machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input. [ABSTRACT FROM AUTHOR]

This paper examines the usefulness of psychophysiological measurements in a biocooperative feedback loop that adjusts the difficulty of an upper extremity rehabilitation task. Psychophysiological measurements (heart rate, skin conductance, respiration, and skin temperature) were used both by themselves and in combination with task performance and biomechanics. Data fusion was performed with discriminant analysis, and a special adaptive version was implemented that can gradually adapt to a subject. Both healthy subjects and hemiparetic patients participated in the study. The accuracy of the biocooperative controller was defined as the percentage of times it matched the subjects' preferences. The highest accuracy rate was obtained for task performance (approximately 82% for both healthy subjects and patients), with psychophysiological measurements yielding relatively low accuracy (approximately 60%). The adaptive approach increased accuracy of psychophysiological measurements to 76.4% for healthy subjects and 68.8% for patients. Combining psychophysiology with task performance yielded an accuracy rate of 84.7% for healthy subjects and 89.4% for patients. Results suggest that psychophysiological measurements are not reliable as a primary data source in motor rehabilitation, but can provide supplementary information. However, it is questionable whether the amount of additional information justifies the increased complexity of the system. [ABSTRACT FROM PUBLISHER]

This paper revisits several concepts and model frameworks that have been in the literature of human–machine interaction and control engineering for up to 50 years. The purposes of the revisit are as follows: 1) to sharpen distinctions between adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control engineering as the terms are currently used in the literature; 2) to define modes of human supervisory adaptation from the control engineering perspective; and 3) to suggest comparative taxonomies for adaptive automation in direct control and in supervisory control. [ABSTRACT FROM AUTHOR]

Three-dimensional video content has attracted much attention in both the cinema and television industries, because 3D is considered to be the next key feature that can significantly enhance the visual experience of viewers. However, one of the major challenges is the difficulty in providing high quality images that are comfortable to view and that also meet signal transmission requirements over a limited bandwidth for display on television screens. The different processing steps that are necessary in a 3D-TV delivery chain can all introduce artifacts that may create problems in terms of human visual perception. In this paper, we highlight the importance of considering 3D visual attention when addressing 3D human factors issues. We provide a review of the field of 3D visual attention, discuss the challenges in both the understanding and modeling of 3D visual attention, and provide guidance to researchers in this field. Finally, we identify perceptual issues generated during the various steps in a typical 3D-TV broadcasting delivery chain, review them and explain how consideration of 3D visual attention modeling can help improve the overall 3D viewing experience. [ABSTRACT FROM AUTHOR]