Applied Ergonomics最新文献

Patient ergonomics advances understanding of patient work to support individuals in managing health and health care, which can be expanded to include health research. While health research enables improvements in care delivery and health outcomes, participant recruitment, enrollment, and retention challenges hamper effective research participation, impede success of research studies, and influence the validity and generalizability of findings. Recent trends in health research create new opportunities to engage patients in research but require careful attention to patient ergonomics and human factors considerations to ensure they successfully address participant needs and contexts. This article describes the research participation process and related patient ergonomics considerations, summarizes patient ergonomic factors identified in the literature that influence participation, discusses how patient ergonomics can be expanded to understand and address challenges to health research participation using two exemplar research programs, and identifies opportunities to include health research participation as part of patient ergonomics.

Order picking tasks require repetitive trunk and upper arms movements that may increase the risk of developing musculoskeletal disorders, particularly among older workers due to the decline of their physical capabilities with aging. We proposed an approach based on a limited number of wearable inertial sensors to assessed exposures to non-neutral trunk and upper arms postures among both older and young workers during their regular work-shifts. The obtained data were processed accordingly to international standards (ISO 11226 and EN 1005-4) to detect the existence of possible differences associated with age-specific working strategies. While the results indicate similar trunk and upper arms movement frequencies in both groups, older workers spend a significantly smaller percentage of time in the most demanding (>60°) postures for both districts. Such findings suggest the adoption of specific strategies to reduce the biomechanical risk which might be originated by a combination of awareness of physical limits and superior working experience. In this context, the instrumental monitoring of upper body in the logistic sector may result useful to highlight critical conditions potentially able to promote the onset of musculoskeletal disorders, thus supporting the decision processes pertaining to workers' health management and aging worker retainment.

Introduction: The aim of this review was to identify, collect, appraise, and synthesise research profiling paramedic job tasks, injuries sustained, and current fitness levels, to guide optimal workplace performance and enhance injury mitigation efforts.

Methods: Following the Preferred Reporting Items for Scoping Reviews, four databases (PubMed, SPORTdiscus, CINAHL, and Embase) were searched using key search terms (derivatives of 'paramedic' and 'injury', 'physical fitness' and 'tasks'). Identified records were screened against eligibility criteria with remaining studies critically appraised.

Results: Of 1675 identified records, 33 were retained. Musculoskeletal injuries exhibited the highest mean injury rate, with sprains and strains the predominant nature of injury, and the back the most frequently injured body part. Among paramedics, handling stretchers and equipment posed the most challenging tasks while for Emergency Medical Services patient extraction was the most physically and mentally demanding task. Male paramedics were generally stronger with more muscular endurance, but less flexibility, than female paramedics. Older paramedics displayed lower levels of strength and flexibility.

Conclusion: Tasks involving stretcher handling and patient extraction are reported as being most strenuous for paramedics leading to musculoskeletal injuries, particularly sprains, strains, and back injuries.

Ectopic pregnancies are uncommon among women presenting for abortion. However, where suspected, rapid referral for definitive diagnosis is essential to prevent harm. We assessed whether implementation of a standardised decision-making tool, an algorithmic representation of the clinical decisions and associated actions defined in policy, in a British abortion service was associated with a reduction in missed opportunities to escalate care where indicated. We compared two cohorts: pre-implementation 01/03/2018-28/02/2019 (n = 2191) and post-implementation 01/08/2019-29/02/2020 (n = 1837). Implementation was associated with improved management: Escalation post-implementation was significantly higher in cases with uncertain menstrual dating (12.8% vs 8.9%), gestation by last menstrual period over 5 weeks (86.9% vs. 23.0%), prior tubal surgery (0.9% vs. 0.3%), and pain (54.1% vs. 44.5%). Missed escalation of care was lower post-intervention than pre-intervention (0.33 % vs. 1.14%, respectively, p = 0.004). Escalation resulting in an ectopic pregnancy diagnosis occurred more often post-implementation [n = 129 (7.0%)] than pre-implementation [n = 116 (5.3%)], p = 0.03.

This study investigated the user affective (Kansei) evaluation of foldable smartphone creases. Twenty-five participants assessed twenty foldable smartphone display prototypes with various multi-crease patterns, for four affective dimensions (crease invisibility, flawlessness, luxuriousness and harmoniousness) using an 11-point rating scale, and, for overall goodness using a 7-point adjective-anchored scale. The adjectives for overall goodness evaluation represented different crease grade categories. Principal component analysis identified a composite measure, termed Affective Quality Score (AQS), which integrates the four affective dimensions. A regression model was developed to predict AQS based on physical features of a multi-crease pattern. Also, a crease grading system was established that determines the adjective grade category of a multi-crease pattern using its AQS. The regression model along with the crease grading system enables predicting and grading the affective quality of a given multi-crease pattern. The research outcomes would support design goal setting and continuous quality improvement for foldable smartphone products.

Background: Fall injuries resulting from trips are a major health concern. Virtual reality (VR) offers an effective way of training obstacle avoidance while walking due to its ability to provide safe and meaningful real-time feedback during rehabilitation. This proof-of-concept study examined the benefit of providing physical feedback during obstacle avoidance gait training using VR.

Methods: Twenty-six young adults walked on an instrumented treadmill while wearing a head-mounted display in two 8-min conditions. Virtual obstacles to be avoided were presented in a VR-only condition and a VR + Perturbation (VR + P) condition where additional rapid belt acceleration simulated tripping on an obstacle.

Results: A lower obstacle collision rate, greater step length and height of the leading foot over the obstacles were found in the VR + P condition compared to the VR-only condition (p < 0.05). Step height of the trailing foot over the obstacles significantly decreased over time during the VR-only condition (p < 0.01) but not during the VR + P condition. The margin of stability significantly improved over time during the VR + P condition only (p < 0.01).

Conclusions: Immediate physical feedback via treadmill belt acceleration can improve obstacle avoidance performance in a virtual environment. Future research is required to examine the generalizability of this finding to other populations and real-world falls.

Cockpit automation has brought significant benefits in terms of mental workload and fatigue. However, the way primary flight instruments are monitored by pilots may be negatively affected by the high confidence in systems. We examined the effects of automation level on mental workload, manual flight performance and visual strategies. Twenty professional pilots performed six landing scenarios at three levels of automation depending on flight director (flight path guidance) and autothrust (automatic management of the speed) engagements. Higher levels of automation increased flight performance and reduced mental workload, but were associated with a decrease in vigilance to primary instruments, particularly flight path indicators and engines' thrust. We also found that gaze entropy was sensitive to pilot role (pilot flying vs pilot monitoring) and automation level. These results confirmed the risks of adverse effects of automation on visual monitoring. Designing procedures for pilots to actively monitor automated cockpit systems should be encouraged.

Road traffic is largely defined by clear rules and laws. However, there are certain situations that are ambiguous and in which explicit communication between road users is needed to resolve such misjudgements or ambiguities. Especially hand gestures can be used to coordinate traffic by conveying different intentions, which refer to one's own behaviour or the change in the behaviour of others. This paper aims to identify such gestures focusing on the pedestrian-driver communication. The paper follows two approaches: Firstly, an experimental study (N = 20) was conducted analysing gestures in predetermined scenarios in a virtual and real environment. Secondly, traffic observations were conducted to analyse and compare natural occurring gestures with those identified in the study. As a result, 18 relevant pedestrian gestures were identified. The type of gestures and the expressiveness of the execution did not differ between the virtual or real study environment. From this, it can be concluded that the virtual environment is an appropriate study environment for identifying and analysing gestures. Furthermore, the type of gestures did not differ between the experimental study and the traffic observations, but the naturally occurring gestures were executed less expressively compared to those from the study. Finally, the paper presents a suitable approach on how pedestrian gestures can be studied and provides a foundation for further research on the communication between pedestrians and automated vehicles (AVs).

The performance of machine learning (ML) algorithms is dependent on which dataset it has been trained on. While ML algorithms are increasingly used for lift risk assessment, many algorithms are often trained and tested on controlled simulation datasets, lacking the diversity of the lifting conditions. Consequently, concerns arise regarding their applicability in real-world scenarios characterized by substantial variations in lifting scenarios and postures. Our study investigates the impact of different lifting scenarios on the performance of ML algorithms trained on surface electromyography (sEMG) armband sensor data to classify hand-load levels (2.3 and 6.8 kg). Twelve healthy participants (6 male and 6 female) performed repetitive lifting tasks employing various lifting scenarios, including symmetric (S), asymmetric (A), and free-dynamic (F) techniques. Separate algorithms were developed using diverse training datasets (S, A, S+A, and F), ML classifiers, and sEMG features, and tested using the F dataset, representing unconstrained and naturalistic lifts. The mean accuracy and sensitivity were significantly lower in models trained on constrained (S) datasets compared to those trained on naturalistic lifts (F). The accuracy, precision, and sensitivity of models trained with frequency-domain sEMG features were greater than those trained with the time-domain features. In conclusion, ML algorithms trained on controlled symmetric lifts showed poor performance in predicting loads for dynamic, unconstrained lifts; thus, particular attention is needed when using such algorithms in real-world scenarios.