To improve the efficiency of medical staff in surgical operations and meet the physiological and psychological needs of surgeons, nurses, and patients during the operations, surgical auxiliary equipment is designed. This paper builds a design research model based on AHP (analytic hierarchy process), QFD (quality function deployment), and Platts conceptual decision matrix (PUGH decision matrix). Firstly, the user requirements are weighed through AHP analysis, and the design elements are prioritized based on the weight values. Then, QFD is used to analyze the design features of surgical auxiliary equipment from the aspects of structure, function, and shape, and a house of quality is established to get the significance of design features. Finally, the PUGH decision matrix is constructed to screen and evaluate multiple schemes, and the optimal design scheme is obtained. From the perspective of user requirements and product design characteristics, the significance of design elements is analyzed and calculated, which guides the design practices to complete the innovative design of surgical auxiliary equipment. The combination of AHP, QFD, and PUGH decision matrices are introduced into the innovative design of surgical auxiliary equipment, effectively avoiding subjective factors in product design, improving the scientific nature of the design, and providing new methods and ideas for the design and research of surgical auxiliary equipment and similar products.

Due to the needs of an aging society, household medical products for the elderly are becoming increasingly important in everyday life. To improve the quality and scientificity of household medical product design for the elderly, this study uses the analytic hierarchy process to improve the evaluation method of household medical product design for the elderly and to promote the vigorous development of household medical products for the elderly. Based on previous research, firstly, this paper summarised the design elements of household medical products for the elderly using questionnaire research and used this to establish a hierarchical analysis model. Secondly, experts and the elderly were invited to rate the subcriteria layers, and the hierarchical analysis software Yaahp was used to calculate the weight values of each criterion layer to arrive at a ranking of the importance of the elements of elderly household medical product design. Finally, the accuracy of the results was verified by three examples of geriatric household blood glucose meters. In this study, the analytic hierarchy process (AHP) was introduced into the evaluation method of the design of household medical products for the elderly, which improved the evaluation system of the design of household medical products for the elderly and pointed out the direction of the design of household medical products for the elderly to continue to optimize and improve in the future. At the same time, it can also provide a reference for the design evaluation of similar products.

The field of complex product design evaluation can attract high ambiguity due to difficulties in establishing indicators and the subjectivity of expert evaluation scoring. Indeed, traditional Kansei Engineering (KE) relies on user requirements and feedback for design evaluation, which may not fully and effectively validate the design evaluation results, let alone determine whether they apply to complex products with more evaluation index systems. To overcome these drawbacks, this study proposes an evaluation method based on Hybrid Kansei Engineering (HKE) modeling for complex product design evaluation. HKE modeling consists of two parts, namely Forward Kansei Engineering (FKE) and Backward Kansei Engineering (BKE). In this study, four electric forklift designs are used as an example. The FKE system adopts the multi-attribute decision evaluation method; obtains the evaluation indexes of the forklift product imagery and then establishes the perceptual word collection; constructs the evaluation index system of the forklift via the Analytic Hierarchy Process (AHP); calculates the weights of the evaluation indexes of each level and their rankings; and calculates the final rankings of the four electric forklift design solutions by adopting the Fuzzy Comprehensive Evaluation (FCE) method. The BKE system adopts eye tracking (ET) to extract the attention time, visual attention hotspot, and other eye movement index data, and the Gray Relation Analysis (GRA) method was used to validate the model to derive the ranking, which verifies the effectiveness and scientific validity of the evaluation method. The results of this study show that the two-way evaluation of HKE modeling can effectively avoid subjective factors in product design, improve the scientific nature of the design, and guarantee the logical rigor of the perceptual design procedure.

The purpose of this study was to compare the variability in quality of life in the area of digital consumption among older adults in urban and rural China during the COVID-19 pandemic. This study proposed a low-cost mixed research method, and the methodology used a quantitative study of a large regional sample combined with a qualitative study of a small regional sample. Data for the large-scale area sample were obtained from the China family panel study (CFPS) dataset, and data for the small-scale regional sample were obtained from Nanjing, China. The quantitative analysis of the large-scale regional sample used the least squares regression analysis (OSL) and propensity score matching (PSM). The qualitative analysis of the small-scale regional sample used the selection optimization and compensation (SOC) model. The findings show that economic income is a direct driver of digital consumption. Digital consumption had a significant positive relationship with the quality of life for urban and rural older adults. In addition, the study established the semantic network relationships of the coping strategies of digital consumption of older adults and their drivers. Finally, the theoretical and practical implications of these findings are discussed in the context of other related studies.

Autonomous systems’ “black-box” nature impedes user trust and adoption. To investigate explainable visualizations’ impact on trust and cognitive states, we conducted a within-subjects study with 29 participants performing high-fidelity driving tasks across three transparency conditions: black-box, standard, and enhanced visualization. Multimodal data analysis revealed that enhanced visualization significantly increased perceived usefulness by 28.5% (p < 0.001), improved functional trust, and decreased average pupil diameter by 15.3% (p < 0.05), indicating lower cognitive load. The black-box condition elicited minimal visual exploration, lowest subjective ratings, and “out-of-the-loop” behaviors. Fixation duration showed no significant difference between standard and enhanced conditions. These findings demonstrate that well-designed visualizations enable balanced trust calibration and cognitive efficiency, advocating “meaningful transparency” as a core design principle for effective human–machine collaboration in autonomous vehicle interfaces. This study provides empirical evidence that transparency enhances user experience and system performance.

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation.