Ergonomics最新文献

This study compared eight upper-extremity risk assessment tools applied to five different manual tasks. The aim is to provide users with insight into how the tools compare and to contribute to the ongoing revision of the ISO 11228-3 Standard. Eight upper limbs risk assessment tools were compared (ACGIH^® HA, DUET, EAWS4, HARM, OCRA index, OCRA checklist, RSI, and RULA). The predictive or concurrent validity of each tool was described and considered. Tool merits and limitations, such as underestimating high action frequencies, static postures, or upper limb areas to study, were described. The ability of the tools to distinguish risk levels was examined. All tools differentiated the risk level of the five tasks, except HARM and RULA, which showed similar risk levels for all tasks. Suggestions are provided to prospective users for choosing tools appropriate to their application, with an emphasis on predictive validity to ensure more reliable risk assessment results.

Mind perception theory explains how people attribute human-like qualities to technology. Drawing on this theory, this study introduces perceived competence and warmth as key dimensions of human-likeness in automated driving systems (ADS). We propose a post-usage model for Level-3 ADS trust and adoption. It extends TAM by incorporating the two human-likeness dimensions, trust, and automated social presence (ASP; feeling of being socially accompanied by automation). We conducted a driving-simulator experiment to manipulate users' perceptions of competence and warmth. The proposed model was then validated using multilevel structural equation modelling with 280 experimental samples. Results show competence and warmth jointly enhance perceived ease of use, usefulness, and ASP, thereby promoting trust and continued usage. Notably, warmth receives greater user attention than competence. Moreover, post-usage trust exerts a stronger impact on continuance intention than original TAM pathways. Our findings inform the design of ADS that foster trust and continued adoption.

Progress in road vehicle automation is tainted by an increased occurrence of passenger motion sickness. Yet, these advances drive the design of car cockpits towards user-centric approaches, such as monitoring passenger well-being. In this framework, the present study aimed to identify robust predictors of motion sickness severity based on data from two experimental studies reproducing a stop-and-go passenger ride. The data from 54 participants tested in a total of 106 identical sessions involving gaze restriction to a visuomotor activity were analysed. Individual characteristics, contextual, behavioural, and physiological parameters were investigated in relation to motion sickness severity. Individual susceptibility had the strongest influence. Pulse rate and the amplitude range of pitch movements of the head additionally exhibited a significant relationship with the progression of symptoms. Such findings open new perspectives for the development of passenger-centric methods to screen early stages of motion sickness and prevent its escalation.

Previous studies found individual variations during long-term heat acclimation, for short-term heat acclimation (STHA), this remains unclear. This study aimed to examine the impact of individual characteristics on thermophysiological adaptations during STHA. Forty-six service members participated in an STHA protocol in 35.1 ± 0.4 °C and 50 ± 4% RH. Gastrointestinal temperature (T_gi), mean skin temperature (T_sk), heart rate (HR), whole-body sweat loss, and subjective scores were recorded and analysed using mixed-effect modelling. Both end-of and change-during fixed work-rate T_gi, T_sk, and HR showed a decline from day 1 to 5 (p ≤ 0.011). Subjective scores improved (p ≤ 0.005). Fat percentage, body surface area-to-mass ratio, body mass, sex, and age showed relationships (p ≤ 0.05) with one or more outcome measures. STHA results in physiological and subjective benefits, although most of the variance remains unexplained by the recorded characteristics. No individual- or group-level time interactions were found during STHA, indicating the military population adapted uniformly to heat.

This study investigated the reliability of a novel casualty emergency evacuation (CEE) test and its associations with physical performance variables. Forty-eight male reservists performed the CEE test, along with physical performance and body composition assessments. The reliability of the CEE was evaluated across two sessions, using analyses of typical error, intraclass correlation (ICC) and coefficient of variation (CV). Pearson's correlation and multiple linear regression (MLR) were applied to identify variables associated with the CEE. The ICC (0.84) and CV (6.77%) indicated that the CEE test demonstrated good reliability. The strongest correlations were observed between the CEE and standing long jump (r = -0.67, p < 0.001), 30 m sprint (r = 0.59, p < 0.001) and countermovement jump (r = -0.55, p < 0.001). In the multiple regression analysis, body height, 60 s push-ups, standing long jump, and 30 s jumps collectively explained 67% of the variance in CEE time.

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.