Traditional ergonomic studies rely predominantly on psychological explanations to address human factors issues such as: work performance, operational safety, and workplace-related risks (e.g., repetitive stress injuries).
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation.
Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world—for example, in the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships.
Using more mobile techniques such as fNIRS and EEG, research may be conducted in more realistic settings including even participation in the actual work being investigated (ex: driving).
These techniques have the advantage of being more affordable and versatile, but may also compromise by reducing the number of areas recorded and the ability to image neural activity from deeper brain regions.
[2] Adaptive automation, a novel neuroergonomic concept, refers to a human-machine system that uses real-time assessment of the operator's workload to make the necessary changes to enhance performance.
[3] Experiments show that a human-robot team performs better at controlling air and ground vehicles than either a human or robot (i.e. the automatic target recognition system).
A developing area of research called brain–computer interfaces (BCIs) strives to use different types of brain signals to operate external devices, without any motor input from the person.
When the user engages in a specific mental activity, it generates a unique brain electrical potential that is processed and relayed into a signal for the external device.
Current active therapy cannot be utilized by patients who suffer complete control loss or paralysis, and do not have any residual motor ability to work with.
With a focus on these underserved patients, a BCI was created that used the electrical brain signals detected by an EEG to control an upper-limb rehabilitative robot.
All eight of the nursing faculty who participated agreed to this much, and that they would recommend that students work with the VRS before performing the IV catheter insertion on real patients.
The central advantage of the VRS program is the availability of a variety of case scenarios, which allow students to increase their awareness of differences in patient responses to IV catheter insertion.
– discuss] Neuroergonomic assessments have tremendous potential for evaluating the psychomotor performance in an individual with a neurocognitive disability or following a stroke or surgery.
These tests could be applied for measuring change following operational procedures such as neurosurgery, carotid endarterectomy, and coronary artery bypass graft.
[10] The Crossmodal Research Laboratory in Oxford is working on developing a system of warning signals to grab the attention of a distracted driver, in an effort to make driving safer for everybody.
[11] Others have evaluated different types of in-vehicle notifications (i.e., auditory icons, speech commands) designed for task management in autonomous trucks for their relevance to separable neural mechanisms; this serves as an effective method to clarify often conflicting findings drawn from behavioral results alone.