User's Mental Workload Research Articles

Influence on the user's emotional state of the graphic complexity level in virtual therapies based on a robot-assisted neuro-rehabilitation platform

BackgroundIn recent years, a remarkable trend in neurorehabilitation is the combination of conventional methods and emerging technologies, such as robotic platforms with virtual reality (VR), Serious Games (SG) and other types of sophisticated graphic interfaces. The aim of the present study is to evaluate the influence on the user's mental workload of the degree of graphic detail present in this kind of environments, comparing the experience of subjects working with two graphical environments with the same physical load but radically different graphic detail levels. MethodsThe same therapy is performed by 52 healthy subjects in two totally different graphic environments, one rich in details and visual stimuli, and its schematic version focusing just in the target graphic elements. Autonomic Nervous System (ANS) activity, related to emotional state, is analyzed through the capture and processing of associated physiological signals. The SAM test is used to assess the subjective perception of the participants in order to establish a relationship with the calculated physiological parameters. ResultsIndexes calculated from the ANS physiological signals show significant differences between the basal state and those corresponding to the performance of the therapies but do not allow discriminating between the effects of performing a therapy based on an environment rich in stimuli and an austere one. In the case of subjective perceptions, based on the results of the SAM test, the subjects perceive significant differences between the basal stage and the therapies, and also between each of the two graphic environments. ConclusionsUsers reflected that a graphic environment rich in detail was more pleasant and attractive than a schematic one. However, this is not reflected in values obtained from their physiological activity, which suggests that more research is needed about the online inference of the emotional state of the subject from the record of his physiological activity.

Effects of galvanic skin response feedback on user experience in gaze-controlled gaming: A pilot study.

Eye-tracking (ET) is one of the most intuitive solutions for enabling people with severe motor impairments to control devices. Nevertheless, even such an effective assistive solution can detrimentally affect user experience during demanding tasks because of, for instance, the user's mental workload - using gaze-based controls for an extensive period of time can generate fatigue and cause frustration. Thus, it is necessary to design novel solutions for ET contexts able to improve the user experience, with particular attention to its aspects related to workload. In this paper, a pilot study evaluates the effects of a relaxation biofeedback system on the user experience in the context of a gaze-controlled task that is mentally and temporally demanding: ET-based gaming. Different aspects of the subjects' experience were investigated under two conditions of a gaze-controlled game. In the Biofeedback group (BF), the user triggered a command by means of voluntary relaxation, monitored through Galvanic Skin Response (GSR) and represented by visual feedback. In the No Biofeedback group (NBF), the same feedback was timed according to the average frequency of commands in BF. After the experiment, each subject filled out a user experience questionnaire. The results showed a general appreciation for BF, with a significant between-group difference in the perceived session time duration, with the latter being shorter for subjects in BF than for the ones in NBF. This result implies a lower mental workload for BF than for NBF subjects. Other results point toward a potential role of user's engagement in the improvement of user experience in BF. Such an effect highlights the value of relaxation biofeedback for improving the user experience in a demanding gaze-controlled task.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots.

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopter's pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas. A mental workload index (MWL) has been here defined as the ratio between the frontal theta and parietal alpha EEG PSD values. Such index has been used for testing and comparing the different avionic technologies. Results suggested that the HUD provided a significant (p<;.05) workload reduction across all the flight scenarios with respect to the other technologies. In addition, the simultaneous use of FC and FG technologies (FC+FG) produced a significant decrement of the workload (p<;.01) with respect to the use of only the FC. Moreover, the use of the SVS technology provided on Head Down Display (HDD) with the simultaneous use of FC+FG and the RO seemed to produce a lower cerebral workload when compared with the use of only the FC. Interestingly, the workload estimation by means of subjective measures, provided by pilots through a NASA-TLX questionnaire, did not provide any significant differences among the different flight scenarios. These results suggested that the proposed MWL cognitive neurometrics could be used as a reliable measure of the user's mental workload, being a valid indicator for the comparison and the test of different avionic technologies.

Cognitive Based Design of a HMI for Telenavigation of A Space Rover

The Human Machine Interface (HMI) design is a critical field of work because no general guidelines or rules have been assessed. In order to aid practitioners to design effective HMIs, different methodologies have been studied. To understand task objectives and plan goal-oriented actions, human operators exploit specific cognitive processes that have to be supported with advanced interfaces. Including cognitive aspects in HMI design allows generating an information flow that reduces user mental workload, increasing his/her situation awareness. This paper will explore a real designing phase of a Graphical User Interface (GUI) for the telenavigation of a space rover that makes the cognitive process of the user a priority in relation to the other development guidelines. To achieve this, the method described in this paper combines a Cognitive Task Analysis (CTA), known as Applied Cognitive Work Analysis (ACWA), with a multi-agent empirical test to ensure the GUI effectiveness. The ACWA allows to evaluate mission scenarios, i.e. piloting the rover on the Mars surface, in order to obtain a model of the human cognitive demands that arise in these complex work domains. These demands can be used to obtain an effective information flow between the GUI and the operator. The multi-agent empirical test, on the other hand, allows an early feedback on the user mental workload aiming to validate the GUI. The result of the methodology is a GUI that eases the information flow through the interface, enhancing operator’s performance.

Adaptive cognitive technical systems.

Adaptive cognitive technical systems are capable of sensing the internal state of its user and of adapting its behavior appropriately to those measurements to improve the usability of the system. One important example of such user state is the user's mental workload level. This paper gives an introduction to the topic of workload recognition and adaptation. It reviews the literature on recognition of workload from physiological signals and on how those user state estimates are employed to improve human-machine interaction.

Toward Adaptive VR Simulators Combining Visual, Haptic, and Brain-Computer Interfaces

The next generation of VR simulators could take into account a novel input: the user's mental state, as measured with electrodes and a brain-computer interface. One illustration of this promising path is a project that adapted a guidance system's force feedback to the user's mental workload in real time. A first application of this approach is a medical training simulator that provides virtual assistance that adapts to the trainee's mental activity. Such results pave the way to VR systems that will automatically reconfigure and adapt to their users' mental states and cognitive processes.

Assessing mental workload and situation awareness in the evaluation of computerized procedures in the main control room

Computerized procedure (CP) system has been developed in nuclear power plant (NPP) instrumentation and control (I&C) system. The system may include normal operating procedures (OPs), abnormal operating procedures (AOPs), alarm response procedures (ARPs), surveillance test procedures (STPs) and/or emergency operating procedures (EOPs). While there are many ways to evaluate computerized procedures design, the user's mental workload and situation awareness (SA) are particularly important considerations in the supervisory control of safety-critical systems. Users’ mental workload and situation awareness may be influenced by human factor issues relating to computerized procedures, e.g., level of automation, dealing with (partially) unavailable I&C, switching to back-up system (e.g., paper-based procedures). Some of the positive impacts of CPs on operator performance include the following: tasks can be performed more quickly; overall workload can be reduced; cognitive workload can be minimized; fewer errors may be made in transitioning through or between procedures. However, various challenges have also been identified with CP systems. These should be addressed in the design and implementation of CPs where they are applicable. For example, narrower “field of view” provided by CP systems than with paper-based procedures could reduce crew communications and crewmember awareness of the status and progress through the procedure. Based on a human factors experiment in which each participant monitored and controlled multiple simulated reactors, this study applied the NASA-TLX instrument for assessing mental workload. For the assessment of situation awareness (SA), the present research used the situational awareness rating technique (SART). In support of summarizing the results of user interface evaluation along multiple dimensions (e.g., workload, SA), we propose advantages for the CPs compared to the paper-based procedures.

Evaluating mental workload while interacting with computer-generated artificial environments

The need to evaluate user behaviour and cognitive efforts when interacting with complex simulations plays a crucial role in many information and communications technologies. The aim of this paper is to propose the use of eye-related measures as indices of mental workload in complex tasks. An experiment was conducted using the FireChief® microworld in which user mental workload was manipulated by changing the interaction strategy required to perform a common task. There were significant effects of the attentional state of users on visual scanning behavior. Longer fixations were found for the more demanding strategy, slower saccades were found as the time-on-task increased, and pupil diameter decreased when an environmental change was introduced. Questionnaire and performance data converged with the psychophysiological ones. These results provide additional empirical support for the ability of some eye-related indices to discriminate variations in the attentional state of the user in visual–dynamic complex tasks and show their potential diagnostic capacity in the field of applied ergonomics.

The Growth of Cognitive Modeling in Human-Computer Interaction Since GOMS

The purpose of this article is to review where we stand with regard to modeling the kind of cognition involved in human-computer interaction. Card, Moran, and Newell's pioneering work on cognitive engineering models and explicit analyses of the knowledge people need to perform a procedure was a significant advance from the kind of modeling cognitive psychology offered at the time. Since then, coordinated bodies of research have both confirmed the basic set of parameters and advanced the number of parameters that account for the time of certain component activities. Formal modeling in grammars and production systems has provided an account for error production in some cases, as well as a basis for calculating how long a system will take to learn and how much savings there is from previous learning. Recently, we were given a new tool for modeling nonsequential component processes, adapting the "critical path analysis" from engineering to the specification of interacting processes and their consequent durations. Though these advances have helped, there are still significant gaps in our understanding of the whole process of interacting with computers. The cumulative nature of this empirical body and its associated modeling framework has further highlighted important issues central to research in cognitive psychology: how people move smoothly between skilled performance and problem solving, how people learn, how to design for consistent user interfaces, how people produce and manage errors, how we interpret visual displays for meaning, and what processes run concurrently and which depend on the completion of prior processes. In the bigger picture, cognitive modeling is a method that is useful in both initial design (it can narrow the design space and provide early analyses of design alternatives), evaluation, and training. But it does not extend to broader aspects of the context in which people use computers, partly because there are significant gaps in contemporary cognitive theory to inform the modeling and partly because it is the wrong form of model for certain kinds of more global questions in human-computer interaction. Notably, it fails to capture the user's fatigue, individual differences, or mental workload. And it is not the type of model that will aid the designer in designating the set of functions the software ought to contain, to assess the user's judgment of the acceptability of the software, or the change that could be expected in work life and the organization in which this work and person fits. Clearly, these kinds of considerations require modeling and tools of a different granularity and form.