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. This paper provides a critical review of the neuroergonomic approach to evaluating physical and cognitive work, particularly in mobile settings. Neuroergonomics research employing mobile and immobile brain imaging techniques are discussed in the following areas of physical and cognitive work: 1) physical work parameters; 2) physical fatigue; 3) vigilance and mental fatigue; 4) training and neuroadaptive systems; and 5) assessment of concurrent physical and cognitive work. Finally, the integration of brain and body measurements in investigating workload and fatigue, in the context of mobile brain/body imaging ('MoBI'), is discussed.

The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the operator chooses to interact with and utilize the robot. This interaction can affect system performance and safety in dynamic ways. The theory of human factors in human-automation interaction has long been studied; however, the formal investigation of these HFs in shared space human-robot collaboration (HRC) and the potential interactive effects between covariate HFs (HF-HF) and HF-RF in shared space collaborative robotics requires additional investigation. Furthermore, methodological applications to measure or manipulate these factors can provide insights into contextual effects and potential for improved measurement techniques. As such, a systematic literature review was performed to evaluate the most frequently addressed operator HF states in shared space HRC, the methods used to quantify these states, and the implications of the states on HRC. The three most frequently measured states are: trust, cognitive workload, and anxiety, with subjective questionnaires universally the most common method to quantify operator states, excluding fatigue where electromyography is more common. Furthermore, the majority of included studies evaluate the effect of manipulating RFs on HFs, but few explain the effect of the HFs on system attributes or performance. For those that provided this information, HFs have been shown to impact system efficiency and response time, collaborative performance and quality of work, and operator utilization strategy.

Objective: The present study used a neuroergonomic approach to examine the interaction of mental and physical fatigue by assessing prefrontal cortex activation during submaximal fatiguing handgrip exercises. Background: Mental fatigue is known to influence muscle function and motor performance, but its contribution to the development of voluntary physical fatigue is not well understood. Method: A total of 12 participants performed separate physical (control) and physical and mental fatigue (concurrent) conditions at 30% of their maximal handgrip strength until exhaustion. Functional near infrared spectroscopy was employed to measure prefrontal cortex activation, whereas electromyography and joint steadiness were used simultaneously to quantify muscular effort. Results: Compared to the control condition, blood oxygenation in the bilateral prefrontal cortex was significantly lower during submaximal fatiguing contractions associated with mental fatigue at exhaustion, despite comparable muscular responses. Conclusion: The findings suggest that interference in the prefrontal cortex may influence motor output during tasks that require both physical and cognitive processing. Application: A neuroergonomic approach involving simultaneous monitoring of brain and body functions can provide critical information on fatigue development that may be overlooked during traditional fatigue assessments.

Advancements in robot technology are allowing for increasing integration of humans and robots in shared space manufacturing processes. While individual task performance of the robotic assistance and human operator can be separately optimized, the interaction between humans and robots can lead to emergent effects on collaborative performance. Thus, the performance benefits of increased automation in robotic assistance and its impact by human factors need to be considered. As such, this letter examines the interplay of operator sex, their cognitive fatigue states, and varying levels of automation on collaborative task performance, operator situation awareness and perceived workload, and physiological responses (heart rate variability; HRV). Sixteen participants, balanced by sex, performed metal polishing tasks directly with a UR10 collaborative robot under different fatigued states and with varying levels of robotic assistance. Perceived fatigue, situation awareness, and workload were measured periodically, in addition to continuous physiological monitoring and three task performance metrics: task efficiency, accuracy, and precision, were obtained. Higher robotic assistance demonstrated direct task performance benefits. However, unlike females, males did not perceive the performance benefits as better with higher automation. A relationship between situation awareness and automation was observed in both the HRV signals and subjective measures, where increased robot assistance reduced the attentional supply and task engagement of participants. The consideration of the interplay between human factors, such as operator sex and their cognitive states, and robot factors on collaborative performance can lead to improved human-robot collaborative system designs.

Standing desks have proven to be effective and viable solutions to combat sedentary behavior among children during the school day in studies around the world. However, little is known regarding the potential of such interventions on cognitive outcomes in children over time. The purpose of this pilot study was to determine the neurocognitive benefits, i.e., improvements in executive functioning and working memory, of stand-biased desks and explore any associated changes in frontal brain function. 34 freshman high school students were recruited for neurocognitive testing at two time points during the school year: (1) in the fall semester and (2) in the spring semester (after 27.57 (1.63) weeks of continued exposure). Executive function and working memory was evaluated using a computerized neurocognitive test battery, and brain activation patterns of the prefrontal cortex were obtained using functional near infrared spectroscopy. Continued utilization of the stand-biased desks was associated with significant improvements in executive function and working memory capabilities. Changes in corresponding brain activation patterns were also observed. These findings provide the first preliminary evidence on the neurocognitive benefits of standing desks, which to date have focused largely on energy expenditure. Findings obtained here can drive future research with larger samples and multiple schools, with comparison groups that may in turn implicate the importance of stand-biased desks, as simple environmental changes in classrooms, on enhancing children’s cognitive functioning that drive their cognitive development and impact educational outcomes.

Major depressive disorder (MDD) has shown to negatively impact physical recovery in a variety of medical events (e.g., stroke and spinal cord injuries). Yet depression assessments, which are typically subjective in nature, are seldom considered to develop or guide rehabilitation strategies. The present study developed a predictive depression assessment technique using functional near-infrared spectroscopy (fNIRS) that can be rapidly integrated or performed concurrently with existing physical rehabilitation tasks. Thirty-one volunteers, including 14 adults clinically diagnosed with MDD and 17 healthy adults, participated in the study. Brain oxyhemodynamic (HbO) responses were recorded using a 16-channel wearable continuous-wave fNIRS device while the volunteers performed the Grasp and Release Test in four 16-minute blocks. Ten features, extracted from HbO signals, from each channel served as inputs to XGBoost and Random Forest algorithms developed for each block and combination of successive blocks. Top 5 common features resulted in a classification accuracy of 92.6%, sensitivity of 84.8%, and specificity of 91.7% using the XGBoost classifier. This study identified mean HbO, full width half maximum and kurtosis, as specific neuromarkers, for predicting MDD across specific depression-related regions of interests (i.e., dorsolateral and ventrolateral prefrontal cortex). Our results suggest that a wearable fNIRS head probe monitoring specific brain regions, and limiting extraction to few features, can enable quick setup and rapid assessment of depression in patients. The overarching goal is to embed predictive neurotechnology during post-stroke and post-spinalcord-injury rehabilitation sessions to monitor patients’ depression symptomology so as to actively guide decisions about motor therapies.