Ergonomics最新文献

Virtual reality (VR) head-mounted display (HMD) was examined for Air Force training modernisation. In Experiment 1 (E1), Novice pilots performed the Overhead break (OHB) manoeuvre and flight proficiency scores were measured and compared to Expert pilots OHB performance in a VR HMD-based simulator. Cybersickness was also measured. Experts performed significantly better on the OHB manoeuvre than Novices. Both groups improved significantly over the course of the experiment and cybersickness was negligible. E1 indicated that the VR HMD flight trainer can be valid, effective and safe for training the OHB maneuvre. Experiment 2 (E2) compared an established Flight Training Device (FTD; not the VR simulator) and Live Flight OHB scores for Novice pilots from E1 to Novices not in E1. There was no difference in FTD and Live Flight scores between both groups. E2 revealed that completion of E1 was insufficient to translate to superior performance in FTD and Live Flight.

With the introduction of conditional automated driving, drivers are freed from continuous control but must remain alert for takeover requests. This study examines how in-cabin temperature (22.5 °C, 25 °C and 27.5 °C) and CO₂ (4200 and 1200 ppm) influence driver drowsiness and physiological responses under conditional automated driving. A driving simulator experiment involving 60 participants was conducted, collecting subjective ratings, eye-tracking and physiological data. Results showed that cooler temperatures were associated with lower drowsiness levels compared to neutral temperatures. However, physiological responses may mainly reflect thermoregulation when temperature varies, obscuring drowsiness-related changes. Further, although CO₂ concentration did not significantly affect subjective drowsiness, higher CO₂ levels attenuated cardiovascular and autonomic activity, suggesting CO₂ effects on physiological responses can emerge before conscious awareness. These findings suggest that climate control systems in automated vehicles should balance comfort, efficiency and driver alertness, while physiology-based driver monitoring systems should incorporate environmental data to detect drowsiness earlier.

The study aimed to develop a comprehensive flight performance evaluation model based on flight parameters, covering the entire flight and applicable to normal and abnormal conditions. Thirty-seven pilots performed one normal traffic pattern flight and one single-engine failure emergency flight using a Cessna-172 simulator. The complete flight was divided into distinct phases - takeoff, climb, cruise, descent, approach/landing, and emergency, with evaluation metrics defined for each phase. The analytic hierarchy process was employed to determine the weights of flight phases and evaluation metrics. Two flight instructors provided ratings of performance after reviewing video recordings of the flights. ChatGPT generated five sets of performance scores based on the flight data. Intraclass correlation coefficient and correlation analyses indicated good consistency across multiple evaluation sources. Significant correlations were found among model-derived scores, instructor ratings, and ChatGPT-generated scores. These findings demonstrate that the model is reliable, and potentially applicable to real-world flight training and operations.

This study examines dynamic personality for in-vehicle voice assistants (IVVAs), addressing a gap in research dominated by fixed assistant personality. Through field investigation of 14 vehicle brands in the Chinese market and a literature review to developed a dynamic personality framework for eight representative in-vehicle tasks. A 2 (personality: dynamic vs. fixed) × 2 (scenario: commuting vs. travel) mixed-design experiment was conducted with 32 participants. The Self-Assessment Manikin (SAM) and the User Experience Questionnaire (UEQ) assessed the effects of assistant personality on driver affect and user experience in autonomous driving. Results showed that compared to fixed personality, dynamic personality significantly improved drivers' pleasure, arousal and dominance across both scenarios. In terms of user experience, dynamic personality enhanced perceived attractiveness, stimulation and novelty but led to lower ratings in perspicuity, efficiency and dependability. These findings highlight the importance of dynamical personality in improving driver affect and optimising the in-vehicle user experience.

Implementing real-time driving anger detection and appropriate intervention are crucial for road safety. A naturalistic driving study (NDS) was conducted to further validate the anger detection method tested in simulated experiments. Thirty-four drivers participated in the tests, each lasting one to two weeks. A smartphone with a self-developed application was used to record the encountered anger-provoking events, drivers' facial expressions, and vehicle kinematic data. Drivers' anger-related traits were collected through questionnaires. A total of 570 events were collected. Abnormal lane-changing, being blocked, and slow driving were the most common anger triggers, mainly occurring during morning rush hours on ring roads and inbound/outbound highways/main roads. The driving anger detection model with the collected data achieved 93.6% accuracy and 93.4% F1 score. Anger-sensitive features and their variations during anger were presented. These findings may enhance drivers' emotional experience in intelligent vehicles and facilitate the development of emotion detection and intervention systems.

To address rising comfort expectations in turbofan aircraft cabins, this study developed a predictive model for multimodal discomfort from combined noise and vibration. The noise/vibration stimuli were generated based on recordings in front and middle cabins during taxiing and cruising and amplitude-modulated to four intensity levels. Using a 6-DOF vibration simulator replicating cabin dynamics, we exposed 26 participants to isolated and combined stimuli. Results revealed vibration attenuated noise discomfort in middle cabin during taxiing, while noise bidirectionally modulated vibration discomfort in middle cabin during taxiing or cruising and front cabin during cruising. Mutual inhibition neutralised cross-modal effects during peak taxiing conditions in middle section. Equivalence discomfort contours quantified the relative impact of each modality, identifying optimisation priorities. A multivariate regression model predicting combined discomfort from modality-specific inputs achieved high accuracy (adjusted R² = 0.819), providing an engineering tool for cabin environment design.

Augmented Reality revolutionises education by enhancing learning with interactive, immersive experiences. However, the impact of long-term AR use, particularly in terms of physical demand, within educational environments remains poorly understood. This study investigates the relationship between AR engagement and physical demand, utilising motion capture technology, NASA Task Load Index, and HoloLens eye-tracking to quantify user posture, engagement, and perceived workload. We hypothesise that prolonged AR interaction results in a change in slouching scores, indicating increased fatigue. The results show a strong correlation between the slouching score and the NASA-TLX physical demand score. Our study lays the groundwork for incorporating predictive modelling to develop proactive physical demand measures.

Despite recent advances in technology use for education and training, the approach to pilot training over the past several decades has largely remained unchanged. Student pilots complete their training in actual aircraft, with very few flight hours conducted in flight training devices. This study aimed to investigate the effectiveness of various levels of simulator fidelity on ab initio pilot training. Thirty student pilots were invited to train using a virtual reality simulator, desktop simulator, or flight training device. Performance was evaluated using a modified Transport Canada Flight Test Guide alongside the NASA Task Load Index, Subjective Stress Scale, and Simulator Sickness Questionnaire, giving insight into mental workload, stress, and experience of simulator sickness, respectively. Findings show potential for virtual reality and desktop simulators regarding training procedural tasks; however, trainees must be aware of the limitations virtual reality and desktop simulators have concerning the training of aircraft handling tasks.

The use of exoskeletons is increasingly considered as a solution to reduce workers' exposure to physical risk factors, such as low-back disorders. The aim of this study was to evaluate the effects of the CORFOR^® occupational soft-back exoskeleton on trunk muscle activity and kinematics during an order picking manual task performed in the field. 10 workers, with at least 4 weeks' experience using the exoskeleton, performed a 1.5-hour order picking task with and without the exoskeleton. Trunk muscle activity, upper-body kinematics and the exoskeleton's acceptance were assessed. Erector spinae muscle activity was significantly reduced by 7.5% with the use of the exoskeleton. Moreover, trunk flexor muscles activity, trunk kinematics, or low-back pain were not affected. Further, the acceptance of the exoskeleton was rated as favourable. Thus, at least in the test company, the integration of the CORFOR^® exoskeleton for order picking tasks is promising.