Ergonomics最新文献

Workers' risks to develop musculoskeletal disorders (MSD) are influenced by work-related and individual risk factors (RF). If workers have different levels of vulnerability (individual RF), managers face the decision whether to foster equality (an equal distribution of work-related RF levels) or equity (an equal distribution of net resulting MSD risk) when designing work plans. We use a mathematical model to assess the consequences of equality and equity policies for workers' MSD risks. We also aim at raising researchers' and practitioners' awareness for these kinds of questions. The model builds upon the assessment of workers' MSD risks using logistic regression. Applying the model to epidemiological data suggests that neither administrative risk control strategy provides a net benefit at the group level as risks can only be shifted between workers but not be reduced. These results suggest that real workforce level risk reductions may require engineering controls to reduce MSD risks.Practitioner summary: Existing epidemiological MSD risk evidence is used to analyse the impacts of policies of workload equality and injury risk equity. The policies have little impact on overall workforce MSD rates. This illustrates how administrative control policies may be ineffective at managing workforce injury risks. Engineering controls to reduce workload levels are recommended instead.

This study investigated whether prior crewed aviation experience and wider demographics influenced Uncrewed Air Vehicle (UAV) operators' propensity to trust and perceived trustworthiness of UAVs. Appropriate trust in automation is essential for managing workload and avoiding user intervention errors that could otherwise have been prevented in UAV operation. Thirty eight UAV operators completed an online survey containing demographic questions, the Propensity-To-Trust Automation (PTT-A) scale, and the System Trustworthiness Scale (STS). No difference was found in PTT-A scores between operators with limited (<100 hours) and substantial (≥100 hours) crewed flight experience. However, those with substantial crewed experience rated UAV systems significantly lower on the STS. Regression analysis showed perceived trustworthiness was significantly predicted by PTT-A facets of competence, benevolence, and integrity. These findings suggest that UAV system design, and processes should be tailored to accommodate operator experiential differences to support trust calibration and system safety.

While the continuous growth of food delivery services brings convenience to customers, this work sector faces challenges related to well-being and performance. This study adopted Work System Model and Transactional Model of Stress and Coping to investigate the significant predictors of performance outcomes among food delivery riders in Bulacan, Philippines. It aimed to examine the effects of individual factors, work-related stressors, ergonomic factors, and coping strategies on performance metrics. The methodology involved a cross-sectional design, covering 270 randomly selected riders. Ordinal Logistic Regression was used to model the causal relationships between the predictor and response variables. The findings reveal common significant factors affecting performance metrics, such as sleep quality, level of mental workload, extreme weather conditions, and years of work experience. These findings contribute to a deeper understanding of the work system of food delivery riders and provide insights for creating policies to enhance well-being and overall performance.

Production engineers increasingly integrate ergonomics intoassembly line design, given the long-term adverse effects on operator health. This paper proposes the Motion Energy Measurement (MEM) system, an extension of the MTM-UAS method for assessing manual tasks. While MTM-UAS standardises task timing through basic motion units, it does not account for physiological workload.MEM addresses this gap by integrating metabolic energy expenditure models into each MTM-UAS motion element, enabling quantifiable assessment of physiological workload. A case study conducted at Bosch Thermotechnology in Portugal, involving a hot water boiler assembly line, revealed a disparity between task duration uniformity (CV $\approx$ 0.5%) and energy variability (CV $\approx$ 11.7%) across operators. These results expose critical ergonomic blind spots in time-based balancing strategies and underline the potential of MEM for real-time identification of high-strain tasks. This work advances ergonomic diagnostics by introducing a scalable, physiology-informed layer to traditional time-motion analysis.

Users increasingly develop emotional connections with AI chatbots that extend beyond utilitarian functions, yet no validated multidimensional scale exists to measure these bonds. This research developed and validated the AI Attachment Scale (AIAS) through two studies: scale development (Study 1) followed by validation and framework testing (Study 2). Study 1 employed exploratory factor analysis (N = 531) to establish a 15-item scale capturing three dimensions: Emotional Support, Separation Distress, and Secure Base. Study 2 used confirmatory factor analysis (N = 375) to validate the scale structure and propose a theoretical framework linking individual differences to AI attachment and behavioural outcomes. Results showed anthropomorphism as the strongest predictor of AI attachment orientations. Attachment anxiety positively predicted AI attachment (β = 0.44), while attachment avoidance negatively predicted it (β = -0.53). AI attachment significantly predicted behavioural intentions (β = 0.50). This research provides a validated measure of human-AI attachment and practical guidance for emotional design in AI chatbots.

Time spend using smartphones is constantly increasing. Portability leads to postures that expose them to musculoskeletal disorders (MSDs). The aim was to study the effect of time of day (morning, afternoon, evening and night) on university students' postures when using their smartphones over the weekend and their link with MSD risk. A cross-sectional survey was conducted among 277 university students (25.3% female, 74.7% male, 17-24 years). SmarTaxo with 41 postures (sitting, standing, lying, walking) and their relative RULA (Rapid Upper Limb Assessment) scores were considered. The overall distribution of postures was: 36.22% sitting, 17.53% standing, 37.67% lying down and 8.57% walking. Six lying and one standing posture observed in the evening and at night are at high MSD risk (RULA score = 6, 23% of total time). The survey highlighted that university students are exposed to MSDs during the weekend day due to awkward postures, especially when lying down at night.

An ergonomic assessment was conducted to quantify the activities and physical demands during the British Army's 8-week Platoon Sergeant and Section Commander Battle Courses (PSBC, SCBC). Twenty PSBC and 18 SCBC male infantry soldiers volunteered. Body Mass (BM) was measured pre- and post-course, with course physical activity levels (PAL), energy expenditure (EE) and sleep profiles quantified using tri-axial accelerometery. The courses were predominately field-based, involving slow and rapid load carriage tasks, digging and moving casualties. Average daily EE (SCBC = 4020 ± 599 vs. PSBC = 3876 ± 525 kcal.day^-1; p>0.05) and BM decreases were similar (SCBC = -3.9 ± 2.9 vs. PSBC = -2.0 ± 2.7 kg; p>0.05). Daily PAL was higher for SCBC than PSBC (2.1 ± 0.3 vs. 2.0 ± 0.3, p=0.041), likely due to greater moderate-vigorous activity levels (p=0.003). Daily sleep durations were variable, but similar across courses (≈5.1 hr.day^-1; p>0.05). These data confirm these courses are arduous and can be used to inform course-specific physical screening tests and training to increase course success.

We compared shoot/don't shoot task (SDST) performance in law enforcement officers (LEOs) and non-officers under normal cognitive load, and under higher load induced by processing a complex dispatch message. We also examined the effects of participants' behavioural activation (BAS) and inhibition (BIS) systems, impulsivity, and working memory on SDST performance. Stimuli were videos of shoot and don't shoot variations performed by three actors. Dependent measures were shoot errors, fail-to-shoot errors, response time, and shooting accuracy. Compared to non-officers, LEOs were more accurate and adapted to make fewer fail-to-shoot errors. They were not faster, and did not make fewer shoot errors. Compared to normal cognitive load, under higher load, participants were quicker and more accurate, but made more shoot errors. The higher cognitive load condition revealed effects that were not present under normal load. Participants making more shoot errors had higher BAS, and BIS than those making fewer or no errors.

This study investigated whether bidirectional transparency, compared to agent-to-human transparency, improved human-agent collaboration. Additionally, we examined the optimal transparency levels for both humans and agents. We assessed the impact of transparency direction and level on various metrics of a human-agent team, including performance, trust, satisfaction, perceived agent's teaming skills, and mental workload. A total of 30 participants engaged in a human-agent collaborative game in a within-subject experiment with five conditions: a 2 (transparency directions: agent-to-human transparency vs. bidirectional transparency) × 2 (transparency levels: reasoning transparency vs. reasoning + projection transparency) factorial design, plus an additional action transparency condition as a control condition. The findings indicated that bidirectional transparency improved task performance without increasing the mental workload. This study recommends a bidirectional transparency mechanism, in which the agent provides transparency to humans regarding its reasoning and predictions, whereas humans offer transparency to the agent regarding their reasoning. Practitioner Summary: This study highlights the importance of bidirectional transparency in human-agent collaboration, demonstrating its effectiveness in enhancing task performance without increasing mental workload. It recommends implementing a mechanism where both humans and agents share transparency information, optimising collaboration outcomes.

3D scanning capture complex ear morphology, but most research focuses on dimensions defined by product design or ergonomic guidelines, often missing crucial 3D details, especially those of ear canal for in-ear product design. This study conducted a statistical shape analysis on the 3D geometry of the cavum concha and the external auditory meatus (EAM) using 1195 scans of Chinese ears. A surface registration method was adopted to standardise and align ear models for shape analysis. Principal component analysis (PCA) generated statistical models of ear shapes, with the most significant variation reflecting the overall width of the cavum concha, explaining 29.63% of the variation. The k-means++ algorithm was employed to classify ear shapes using the first 15 principal component scores, identifying four shape categorisations. Significant variations and shape modes of ear shape were identified, and the derived statistical shape models provide essential 3D references for the ergonomic design of ear-related products.