Human Factors最新文献

Objectives: This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

Background: During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

Method: Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

Results: The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

Conclusion: Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

Application: Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

Objective: To determine whether (i) low back loads and/or (ii) kinematic coordination patterns differed across theoretical expert, contextual expert and novice groups when completing both generic and occupation-specific lifts.

Background: Experience has been proposed as a factor that could reduce biomechanical exposures in lifting, but the literature reports mixed effects. The inconsistent relationship between experience and exposures may be partially attributable to the broad classification of experience and experimental lifting protocols not replicating the environment where experience was gained.

Methods: Purposive sampling was used to recruit 72 participants including theoretical experts (formal training on lifting mechanics), contextual experts (paramedics), and novices. Participants performed 10 barbell and crate (generic) lifts, as well as backboard and stretcher (occupation-specific) lifts while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear loads normalized to body mass, as well as kinematic coordination patterns, were calculated as dependent variables.

Results: No significant differences in low back loads were observed across expertise groups. However, significant differences were seen in kinematic coordination patterns across expertise groups in occupation-specific lifts, but not in generic lifts.

Conclusion: Increasing expertise is unlikely to minimize low back loads in lifting. However, contextual expertise did influence lifting kinematics, but only when performing occupationally specific lifts.

Application: Contextual expertise may help lifters adopt lifting kinematics that enhance the tolerance of their musculoskeletal system to withstand applied loads, but does not seem to reduce the applied low back loads relative to noncontextual expert groups.

Objective: This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.

Background: Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.

Method: This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.

Results: The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.

Conclusion: There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.

Application: Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

Objective: The aim was to systematically evaluate the usability of all public electric vehicles (EV) direct current fast chargers (DCFC) in the San Francisco region.

Background: To achieve a rapid transition to EVs, a highly reliable and easy to use charging infrastructure is critical to building confidence among consumers.

Methods: The functionality and usability of all 182 open, public DCFC charging stations with CCS connectors (combined charging system) in the 9 counties of the Bay Area were tested (655 electric vehicle service equipment (EVSE) ports). An EVSE was classified as functional if it charged an EV for 2 minutes.

Results: Overall, 73.3% of the 655 EVSEs were functional. The causes of the nonfunctioning EVSEs (23.5%) were blank or unresponsive screens or error messages; payment system failures; charge initiation failures; network failures; or broken connectors. In addition, the cable was too short to reach the EV inlet for 3.2% of the EVSEs. A random sampling of 10% of the EVSEs, approximately 8 days after the first evaluation, found no overall change in functionality.

Conclusions: The level of functionality found with field testing conflicts with the 95-98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. There is a need for precise and verifiable definitions of uptime, downtime, and excluded time, as applied to public EV chargers.

Application: The level of failure of the existing public EV DCFC charge infrastructure highlights the importance of improving the system design and maintenance to improve adoption of EVs.

Objective: This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored.

Background: EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior.

Method: A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15 seconds before the emergency vehicle overtook them.

Results: Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed.

Conclusion: EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning.

Application: The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings.

Objective: To examine the effect of concurrent physical and cognitive demands as well as age on indicators of muscle fatigue at the wrist.

Background: There are few studies examining risk indicators for musculoskeletal disorders associated with work-related physical and cognitive demands that often occur simultaneously in the workplace.

Methods: Twenty-four gender-balanced older and 24 gender-balanced younger (mean age 60 and 23 years) participants performed four 30 min dual tasks. Tasks differed by the muscular load level during force tracking: 5% and 10% of maximum voluntary contraction force (MVC) and concurrent cognitive demands on the working memory: easy and difficult. Muscle fatigue was assessed by MVC decline and changes in surface electromyography (increased root mean square: RMS, decreased median frequency: MF) at the extensor digitorum (ED) and extensor carpi ulnaris (EU).

Results: A decline in MVC was found in all participants when tracking was performed at 10% MVC (mean ± SD: 137.9 ± 49.2 - 123.0 ± 45.3 N). Irrespective of age, muscular, or cognitive load, RMS increased (ED 12.3 ± 6.5 - 14.1 ± 7.0% MVE, EU 15.4 ± 7.6 - 16.9 ± 8.6% MVE) and MF decreased (ED 85.4 ± 13.6 - 83.2 ± 12.8 Hz, EU 107.2 ± 17.1 - 104.3 ± 16.7 Hz) in both muscles. However, changes in MF of EU tended to be more pronounced in the older group at higher cognitive and lower muscular load, without reaching statistical significance.

Conclusion: Maximum voluntary contraction indicated no interaction between muscle fatigue, cognitive load, or age. However, the tendencies toward altered muscle activity due to an increase in cognitive load and older age suggest muscular adaptations while maintaining tracking performance during the onset of fatigue signs in the sEMG signal.

Application: If the tendencies in muscle activity are confirmed by further studies, ergonomic assessments in industrial workplaces should consider cognitive load and age when describing the risk of musculoskeletal disorders.

Objective: To evaluate the effectiveness of passive back-support exosuit on postural control and cognitive performance during a fatigue-inducing posture maintenance task.

Background: Wearable support systems (exoskeletons/exosuits) reduce physical demands but may also influence postural control and cognitive performance by reducing muscular fatigue.

Method: Eighteen participants visited on two different days to test an exosuit system and performed dual-task cognitive assessments based on human information processing (information acquisition, information integration, and action implementation) while maintaining a 35° trunk flexion posture for 16 minutes. Center-of-pressure (CoP), cognitive performance, and perceived workload were recorded, while erector spinae muscle activity was captured to quantify muscle fatigue.

Results: The exosuit was effective in reducing erector spinae muscle fatigue during the static posture maintenance task (61% less in Δmedian frequency: -9.5 Hz (EXO-Off) versus -3.7 Hz (EXO-On)). The fatigue-inducing task increased CoP velocity as a function of time (29% greater: 9.3 mm/sec (pre) versus 12.0 mm/sec (post)), and exosuit use decreased CoP velocity (23% less: 12.1 mm/sec (EXO-Off) versus 9.4 mm/sec (EXO-On)). The exosuit was also effective at mitigating cognitive degradation, as evidenced by a higher hit-to-signal ratio (8% greater: 81.3 (EXO-Off) versus 87.9 (EXO-On)) in the information integration task and reducing perceived workload in all stages of human information processing.

Conclusion: Exosuit provided benefits of postural control and information integration processing during a 16-min static posture maintenance task.

Application: Torso exoskeletons/suits can have positive implications for occupations with concurrent physical and cognitive demands.

Objective: This study examines low-, medium-, and high-performing Human-Autonomy Teams' (HATs') communication strategies during various technological failures that impact routine communication strategies to adapt to the task environment.

Background: Teams must adapt their communication strategies during dynamic tasks, where more successful teams make more substantial adaptations. Adaptations in communication strategies may explain how successful HATs overcome technological failures. Further, technological failures of variable severity may alter communication strategies of HATs at different performance levels in their attempts to overcome each failure.

Method: HATs in a Remotely Piloted Aircraft System-Synthetic Task Environment (RPAS-STE), involving three team members, were tasked with photographing targets. Each triad had two randomly assigned participants in navigator and photographer roles, teaming with an experimenter who simulated an AI pilot in a Wizard of Oz paradigm. Teams encountered two different technological failures, automation and autonomy, where autonomy failures were more challenging to overcome.

Results: High-performing HATs calibrated their communication strategy to the complexity of the different failures better than medium- and low-performing teams. Further, HATs adjusted their communication strategies over time. Finally, only the most severe failures required teams to increase the efficiency of their communication.

Conclusion: HAT effectiveness under degraded conditions depends on the type of communication strategies enacted by the team. Previous findings from studies of all-human teams apply here; however, novel results suggest information requests are particularly important to HAT success during failures.

Application: Understanding the communication strategies of HATs under degraded conditions can inform training protocols to help HATs overcome failures.

Objective: Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness.

Background: Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework.

Method: We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors.

Results: The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states.

Conclusion: The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters.

Application: The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

Study aim: This study aims to describe the transition-in-care work process for sepsis survivors going from hospitals to home health care (HHC) and identify facilitators and barriers to enable practice change and safe care transitions using a human factors and systems engineering approach.

Background: Despite high readmission risk for sepsis survivors, the transition-in-care work process from hospitals to HHC has not been described.

Methods: We analyzed semi-structured needs assessment interviews with 24 stakeholders involved in transitioning sepsis survivors from two hospitals and one affiliated HHC agency participating in the parent implementation science study, I-TRANSFER. The qualitative data analysis was guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework to describe the work process and identify work system elements.

Results: We identified 31 tasks characterized as decision making, patient education, communication, information, documentation, and scheduling tasks. Technological and organizational facilitators lacked in HHC compared to the hospitals. Person and organization elements in HHC had the most barriers but few facilitators. Additionally, we identified specific task barriers that could hinder sepsis information transfer from hospitals to HHC.

Conclusion: This study explored the complex transition-in-care work processes for sepsis survivors going from hospitals to HHC. We identified barriers, facilitators, and critical areas for improvement to enable implementation and ensure safe care transitions. A key finding was the sepsis information transfer deficit, highlighting a critical issue for future study.

Application: We recommend using the SEIPS framework to explore complex healthcare work processes before the implementation of evidence-based interventions.