Browse Topic: Nervous system
A study of mental workload and the resultant cognitive-motor behavior is essential to understanding the intrinsic limitations of the human information processing system, the results of which have impact on the design of safety-critical systems. While the effects of increased task demand on mental workload and the quality of cognitive-motor performance has been previously investigated, it remains unclear how system controllability (i.e., expected handling qualities) impacts perceptual workload and performance. Furthermore, traditional EEG spectral metrics lack the temporal specificity to capture dynamic workload. Consequently, the purpose of this experiment was to examine objective brain dynamics, task performance, and subjective ratings during piloting tracking tasks of varying complexity while also challenging participants with different expected levels of handling qualities. Our results revealed a trend suggestive of increasing mental workload related to increased task complexity and varying levels of expected handling qualities. To examine dynamic operator workload with increased temporal fidelity, we introduce a time-resolved cross-correlation based approach to assess synchronous dynamics between cortical activity and behavioral performance. The findings herein highlight the practical significance of including analyses of the time domain in workload assessment, in addition to the functional utility of a combination of metrics in the study of the temporally linked cognitive-motor output associated with increased mental workload.
This SAE Information Report provides definitions and discussions of key terms concerning driver drowsiness and fatigue, and basic information on measuring drowsiness and fatigue. It also includes information and concepts for driver drowsiness as they relate to the safe operation of a vehicle. The key driver drowsiness and fatigue causal factors include the following: (1) sleep quality and quantity, (2) time of day, (3) time awake, (4) time on task (modulated by characteristics of the driving task), (5) task-related fatigue (variations of arousal levels related to task underload and overload), and (6) combinations of these factors. Medical conditions, medication, alcohol, or drugs exacerbate drowsiness; however, the discussion in this report is limited to fatigue concepts. This report has two primary outputs: (1) definitions and discussions of key terms concerning driver drowsiness and fatigue, and (2) basic information on measuring drowsiness and fatigue and its effects on the safe operation of a vehicle. These include the physiological and cognitive effects of driver drowsiness and fatigue on driving safety. Examples of effect of driver drowsiness and fatigue on driving safety include those related to vehicle control, operator vigilance (sustained attention), reaction times (object and event detection and response), situational awareness, physiological indicators, subjective assessments, and combinations thereof. For definitions of driving performance measures, refer to SAE J2944. This report applies to all worldwide motor vehicle passenger cars and light trucks, as well as heavy trucks, buses, motorcycles, and mopeds. The intended users of the document are practitioners and researchers in the automotive industry, academia, and other organizations with interest in driver drowsiness and fatigue, driving and driver performance assessment, and road safety.
Rice University neuroengineers have created a tiny surgical implant that can electrically stimulate the brain and nervous system without using a battery or wired power supply.
Two qualified test pilots performed a target tracking flight task on a Bell 205 helicopter. Cooper-Harper handling quality ratings confirmed that pilot compensation was proportional to task difficulty. Pilot control activity was measured using the Dynamic Interface Modeling and Simulation System Product Metric (DIMSS-PM) to quantify the number and amplitude of control deflections during each trial. Inter-beat interval measures of heart rate and heart rate variability were also computed to evaluate the pilot's autonomic nervous system (ANS) response to task workload. The DIMSS-PM was positively correlated with task difficulty, as expected based on the dynamics of target motion for each difficulty level. By comparison, the mean and high-frequency (HF) variability of heart beat intervals were negatively correlated with task difficulty, suggesting an increase in ANS arousal with increased pilot workload. Pilot-specific differences were found in the time-dependent relationship between DIMSS-PM, mean heart beat interval, and HF variability, indicating that control activity and heart rate metrics provide asynchronous and complementary information about pilot workload during helicopter flight. NOTATION ANOVA Analysis of Variance ANS Autonomic Nervous System DIMMS-PM Dynamic Interface Modeling and Simulation System Product Metric ECG Electrocardiogram FBW Fly-By-Wire FRL Flight Research Laboratory HR Heart Rate HRV Heart Rate Variability HQR Handling Qualities Rating NRC National Research Council Canada RRI Inter-beat (R-R) Interval RMS Root Mean Square RMSSD RMS of Successive Differences in RRI SDNN Standard Deviation of RRI HF High Frequency (0.15 - 0.4 Hz) LF Low Frequency (0.04 - 0.15 Hz)
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