Neuroergonomics: A Cognitive Neuroscience Approach to Human Factors and Ergonomics
Is the device presenting an appropriate level of information that surgeons can effectively concentrate for a 2 hour, 6 hour, or 10 hour surgery? Is the device presenting so much unnecessary information that the surgeon is less cognizant of necessary information? Does the common practice of playing music during surgery increase the mental workload of the surgeon, requiring adapting the UI for the actual use environment? These are all important questions to consider when designing and evaluating all types of systems, not just surgical robotics, where operator performance is crucial to outcomes and would otherwise be hidden without neuroergonomic methods.
The next topic looks at creating systems that are less sensitive to human error, and, if error occurs, are able to minimize the potential effects retroactively. Each of these catastrophic events were the result of human error and potentially could have been avoided with better system designs. Every task has room for error to occur, which is why this is such an integral field of study.
While we currently can't attach an EEG to every person to monitor for errors, knowing that the brain monitors and reacts to errors has far-reaching implications. Alternatively, if a nurse accidentally set up the wrong dose or titration schedule on an infusion pump, the EEG could detect an ERN and alert the user to an error.
The applications for this type of connected devices are essentially endless. Behaviour change. It may be one of the most common buzzwords in digital health right now, but how do we actually change behaviour through digital interventions? Many studies have used EEG because of its ease of recording and relative unobtrusiveness compared, say, to secondary tasks or subjective questionnaires. EEG also has the property of being a very high bandwidth measure, offering the possibility of sampling the human operator at up to about 30 Hz Wilson and Russell, Workload adaptive systems need to assess operator state in real time, or near real time, so that task allocation or restructuring can be implemented in cases of overload or underload.
A number of different statistical and machine learning techniques have been used for this purpose. These include discriminant analysis Berka et al. Implementing neuroergonomic adaptive systems in real settings poses significant challenges. A major issue concerns the detection and removal of artifacts in real time. Furthermore, while initial success has been achieved in using computational techniques to classify workload on the basis of EEG and other neuroergonomic measures, the reliability and stability of these methods within and across individuals needs to be more rigorously tested Wang et al.
Finally, the operational community must be involved in the design of adaptive systems to ensure user acceptance and compliance. Both physical and cognitive neuroergonomics have helped advance our understanding on the role of the human brain during physical and cognitive work, respectively. High cognitive demands can influence physical work; and physical activity can in turn influence cognitive processing. In comparison to traditional evaluation techniques in either physical or cognitive ergonomics domain, neuroergonomic methods offer a great advantage in assessing these combined demands.
For example, using EEG signals Kamijo et al. They suggested that exercise influenced the amount of attentional resources devoted to a given task and that the changes in P amplitude followed an inverted U -shaped behavior of differences in exercise intensity. When examining the impact of cognitive demand on physical capacity, a few studies have attributed decreased muscle endurance in presence of a cognitively stressful situation to lower motivation Marcora et al.
In particular, using fNIRS to monitor cerebral oxygenation during handgrip exercises, Mehta and Parasuraman demonstrated that concurrent handgrip exercises in cognitive stressful conditions were associated with lower oxygenated hemoglobin levels in the bilateral prefrontal cortex at exhaustion when compared to the handgrip exercises at the same intensity levels i. Such studies are also needed so as to develop evaluation tools surveys, heuristic checklists that are predictive of the neural and physiological cost associated with optimizing work tasks, which can be used by designers or supervisors to quantify operator workload and fatigue.
In this paper, we discussed the merits and disadvantages of the available neuroimaging techniques applicable to neuroergonomics and a key theme identified was the lack of studies evaluating neural bases of mobile work, particularly in the physical neuroergonomics domain. Recent efforts in developing mobile brain imaging MoBI techniques, which consider the physical and environmental impact on human cognitive processing, show great promise. For example, Gramann et al.
In particular, their MoBI investigation included simultaneous brain-body measurements from a channel EEG system and kinematic and kinetic outcomes that are otherwise employed during conventional gait biomechanics using motion capture systems and force plates Gwin et al. While developing an ideal MoBI system may be a challenging goal, understanding current limitations in mobile brain-body imaging and addressing them, albeit painstakingly, is a critical step toward achieving this goal.
Future investigations can also include developing similar mobile brain-body imaging systems for hemodynamic neuroimaging techniques, utilizing either fNIRS or TCDS to provide brain imaging measures, and using peripheral measurements such as heart rate and blood pressure to document physiological whole-body responses. Ergonomics has long since moved from being a science of improving work efficiency to now being focused on enhancing well-being while improving systems performance. To effectively understand how humans interact with work systems, it is not only important to ask how well they perform, but also why they perform a certain way.
Neuroergonomics have helped fill in the gaps on the neural bases of both physical and cognitive performance that were left unanswered with traditional ergonomic assessments. In this review we discussed the recent developments and adoption of neuroergonomic methods and applications in investigating physical, cognitive, and combined physical and cognitive work. We also reviewed the applicability and feasibility of neuroimaging techniques in evaluating mobile work environments. Both authors contributed equally to this work.
Ranjana K. Mehta performed the literature review on neuroergonomics applications to physical work and Raja Parasuraman performed the literature review on cognitive neuroergonomics. Both authors discussed the reviewed implications and commented on the manuscript at all stages. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. National Center for Biotechnology Information , U.
Journal List Front Hum Neurosci v. Front Hum Neurosci. Published online Dec Author information Article notes Copyright and License information Disclaimer. Received Aug 23; Accepted Dec 5. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.
Neuroergonomics: a review of applications to physical and cognitive work
No use, distribution or reproduction is permitted which does not comply with these terms. This article has been cited by other articles in PMC. Abstract Neuroergonomics is an emerging science that is defined as the study of the human brain in relation to performance at work and in everyday settings. Keywords: physical work parameters, physical fatigue, mental fatigue, vigilance, training, neuroadaptive systems.
Open in a separate window. Table 1 List of neuroergonomic techniques and their major features. PHYSICAL WORK Ergonomics began as the science of work to maximize productivity, particularly in physical work environments, but has since then expanded to become a scientific discipline concerned with the understanding of the interactions among humans and other elements of a system, in order to optimize human well-being and overall system performance.
Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Aaslid R. Alterations in cerebral autoregulation and cerebral blood flow velocity during acute hypoxia: rest and exercise.
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