International Journal of Industrial Ergonomics最新文献

One-handed carrying is a demanding and understudied form of manual material handling (MMH). Existing studies have not adequately considered the roles of body fat and physical fitness on the psychophysical and physiological responses of individuals performing one-handed carrying. A laboratory treadmill study involving 16 male and 16 female participants was completed to characterize the physiological and psychophysical responses incurred by one-handed MMH on a flat and inclined surface. Participants walked at a speed of 3.2 km/h for a distance of 96.5 m over four experimental conditions: No-load and flat surface [NF], no-load and inclined surface [NI], load and flat surface [LF], load and inclined surface [LI]). The results indicated generally consistent main effects of body fat, physical fitness, load, and incline on psychophysical and physiological responses, highlighting the inadequacy of current MMH guidance in addressing the impact of body fat percentage and physical fitness on one-handed carrying task performance.

This study investigated how text markers (TMs) and font size (FS) affect the reading experience. College students (N = 45) read textual material presented in four TM formats (underline, highlight, margin note at right, and margin note at end) and three FSs (8 pt, 12 pt, and 14 pt). Their reading experience (immersion, social presence, and satisfaction) was assessed through subjective postreading ratings and interpreted through eye-tracking data. The results showed that different forms of TM affected the reading experience. Effects of TMs on immersion, social presence, and satisfaction were identified, along with interaction effects between TM and FS on immersion, first fixation duration, and total fixation duration ratio. Specifically, reading experience at smaller FSs (8 pt) was more likely to be influenced by TMs. Regarding eye-tracking metrics, first fixation duration was positively correlated with immersion, and total fixation duration ratio was negatively correlated with immersion. Although no correlation was found between immersion and social presence, a negative correlation was found in the actual reading process (i.e., the stronger the perception of the social presence of TMs, the lower the immersion). These findings can provide insights useful for the design of TMs in social reading from a user-experience perspective.

Pilot performance is almost the last line of defense in aircraft safety. Recent years have seen a surge in research aimed at utilizing eye-tracking technology to predict pilot performance, enhancing aviation safety margins. A decline in pilot performance is often attributed to either misdirected attention towards irrelevant tasks or inefficient information processing owing to limited attention resources. Previous research has shown that eye-tracking data can effectively capture these issues and provide accurate performance predictions. Nevertheless, the existing studies either focus on attention distribution or attention resources separately, neglecting the complex interactions between them. To address this gap, our study proposes a synthesized Flashlight model-based eye-tracking analysis for pilot performance prediction, integrating the two perspectives. Accordingly, the combined AOI-gaze metrics are proposed to offer a more nuanced analysis of information processing across specific Areas of Interest (AOIs), thereby enhancing the analysis of gaze metrics. We examined the efficacy of the combined AOI-gaze metrics in the Gradient-boosted decision trees(GBDT) model for pilot performance prediction and compared them with other widely used eye-tracking metrics in a simulated flight experiment case study. Moreover, we employed the SHapley Additive exPlanations (SHAP) method to identify the most influential eye-tracking measurements for pilots’ performance prediction. The result demonstrated that the selected eye-tracking measurements obtained the highest accuracy in performance prediction.

Inter-subject variability and seat conditions are complex and may affect the dynamic responses of the occupant-seat system to vibration. This research was aimed to clarify the contributions associated with the occupant and the seat to seat transmissibilities and thus developed an optimized artificial neural network model with the genetic algorithm to represent the cross-axis coupling and nonlinearity of seat transmissibilities with various seat conditions. Different vibration magnitudes, backrest inclinations, cushion thicknesses, frequencies and the mass of 12 subjects were set as the input parameters to predict the vertical in-line and horizontal cross-axis transmissibilities. For the predictive performance metrics (RMSE and R2), the mean values (0.118 and 0.889) were obtained for both seat transmissibilities within the testing data sets from BP-ANN models, and those with GA-BP-ANN models were optimized with 0.072 and 0.947, respectively. The seat transmissibility predicted from the model exhibited resonance behavior similar to that observed in the whole-body vibration test. With the optimization of the genetic algorithm, GA-BP-ANN models can provide enhanced predictions of the cross-axis coupling and nonlinearity of seat transmissibilities when compared to BP-ANN models.

The traditional prototype method only relies on the basic dimensions of the human body such as bust girth and back length to make the prototype pattern, ignoring the differences of human body details dimensions. In order to improve the fit of garments, the research on intelligent generation technology of individualized garment patterns has become one of the hot spots in the field of clothing. Based on the 3D body scanning technology, we proposed a method for generating prototype pattern based on body shape classification. The proposed method takes as inputs the body parameters (width, thickness and angle), while the output of the method is one of the four pattern generation rules-"Y" shape, "V" shape, "I" shape, or "X" shape. 207 subjects were divided into four categories based on the body angle parameters, namely "Y" shape, "V" shape, "I" shape and "X" shape, and a shape recognition model was built. The pattern database is required to obtain pattern generation rules by 3D point cloud reconstruction and flattening. Combined with the human body shape parameters, the mathematical formulas of the feature points on the pattern landmarks are built. The accuracy of body shape recognition model is 97.4%. The error analysis of the predicted pattern parameters shows that 90% of the pattern feature points have a goodness of fit above 0.7. In the 5 main landmarks, the proportion of pattern within the absolute error range is more than 80%, indicating that the prediction effect of the pattern is good. This method can be applied to the process of automatic pattern generation system based 2D measurement to improve work efficiency.

Artificial intelligence (AI) agents are integral components of modern intelligent decision support systems (IDSS), providing their capability to assist in decision-making processes. Understanding the influence of AI involvement on human responses within these systems is crucial for fostering appropriate reliance on AI advice and ensuring a comfortable user experience. This study delves into the impact of AI agents on decision change, correct rate change, confidence rate change and pleasure rating among users. It explores the main and interactive effects of user proficiency and AI agent capability on user responses. A block-factorial experiment involving 45 participants was conducted. The results indicated that high-capability AI agents were associated with substantial decision change, correct rate change and confidence rate change. Participants with higher proficiency, both subjectively and objectively, exhibited reduced reliance on AI agents for task execution. Notably, the involvement of AI agents led to a slight decline in correct rates for participants of high competence (i.e., participants' independent correct rate exceeds AI agent capability), while still enhancing their confidence levels. Additionally, agents lacking human-like embodiment tended to enhance participants’ pleasure ratings. Individual differences in gender, collaboration experience with AI agent, and stereotype on AI agent capability also shaped the decision changes and pleasure ratings of participants. These findings underscore the presence of complementary competence interaction between user and AI agent within IDSS, and can be applied in designing capability of advisory AI agents with consideration of user proficiency and individual characteristics.

Relevance to industry

The findings can be used as guidelines for designing AI agents and specifying their application strategies to integrate the assistance of AI agents effectively in intelligent decision support systems.

Background

Adopting patient transfer assistive devices in healthcare is challenging, and poor decision-making can lead to low adoption. This study aims to demonstrate a technique of the decision-making process of selection of patient transfer assistive devices based on work-related musculoskeletal disorders (WMSDs) risk, nurses’ instantaneous emotions, and perceptions.

Methods

A case study based on four different patient transfer assistive devices is used to demonstrate this technique. Seven nurses were recruited. Three confidence ellipse graphs were plotted: the intention of the use scores vs (1) National Aeronautics and Space Administration Task Load Index (NASA-TLX) overall scores, (2) Rapid Entire Body Assessment (REBA) scores, and (3) valence scores of the nurses.

Results

When a large ellipse is represented by an intervention, it suggests poor agreement among users regarding the intervention, whereas a small ellipse indicates a strong consensus. The upper left quadrant, where the intention of use is high and REBA, NASA-TLX, and valence scores are low, is the most optimal location for selecting a device. In the case study, the motorised transfer was identified as the best device as the datasets were located there.

Conclusions

Using this tool allows for the objective selection of patient transfer assistive devices, which can then be communicated to all stakeholders involved. Additionally, the tool helps to identify areas for improvement within each intervention.

The development of novel technologies has resulted in changes in the functions offered by head-up displays (HUDs), which differ considerably from conventional digital dashboard displays. This study aimed to create a scientific framework for automotive services for automobile manufacturers to review their service models and solutions as they transition to developing autonomous vehicles. This study used the two-dimensional Kano quality model to identify and classify quality characteristics of HUDs. A Kano questionnaire survey was conducted to collect data on customer satisfaction with different quality characteristics. These characteristics were then ranked in the order of their importance to consumers, thereby identifying quality characteristics that must be prioritized for providing high-quality services with HUDs. Data were collected on their emphases on and satisfaction with 32 quality characteristics belonging to seven factors across two dimensions, namely tangible specific functions and intangible value-added services. The seven factors were speed and distance support, driver alerts, information and communication, display settings, active matching, automatic systems, and software–hardware integration. A statistical analysis was performed to determine the presence of significant differences in the customers’ perception of the quality characteristics. On the basis of the analysis results, 11 quality characteristics were classified as attractive quality, four as one-dimensional quality, six as must-be quality, seven as indifferent quality, and four as reverse quality. By referencing the classification of quality characteristics in their products, automotive companies can adopt the product improvement priority scheme proposed in this study to effectively enhance customer satisfaction with their services.

Wearability of clothing is important for enhancing work efficiency and resistance among industrial workers. In this study, we developed wearable suit designs for shoulder and upper limb support when lifting objects considering the wearability of the user in the development stage. We provide basic data on the wearability of functional clothing and wearable suits, with emphasis on improving safety and work efficiency. Two distinct wearable suit designs, namely long- and short-sleeved, were developed and evaluated with a primary focus on wearer comfort. Employing a comprehensive approach encompassing textile analysis, performance assessment, brainwave investigation, and satisfaction surveys, this study provides objective insights for refining the wearability in wearable suit development. A short-sleeved design is recommended for the development of wearable suits tailored to assist industrial workers in tasks requiring muscle strength for heavy loads and repetitive motions. Our results provide foundational data on the wearability of robotic suits in a bid to enhance the safety and efficiency of industrial workers.

When using electronic devices, older adults usually face challenges associated with difficulties in understanding icons. This study investigated age-related differences in icon identification using various design principles (Experiment 1), as well as the impacts of adding text labels (Experiment 2) and learning strategies (Experiment 3) on improving the performance of older adults. Participants, including older adults (aged 60 or older) and younger individuals, were asked to identify icons matching previous descriptions. The results revealed that the identification performance between older and younger adults varied across icon categories. The addition of text labels resolved differences in icon comprehension related to age of participant and design principles. Learning strategies had diverse effects on the identification performance of older and younger participants, depending on the icon category. These findings provide guidance for designers to apply effective principles and identify optimal strategies to support older adults in the identification and understanding of icons.