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In the research of industrial robot design, designing using only the perceptual thinking and creativity of an industrial designer or overemphasizing the intervention of quantitative data research in the field of emotional cognition is relatively one sided. In this article, research on how to combine the above two aspects effectively will be conduct...
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... final scheme was confirmed through substantial work, including group discussions, selection, extraction, comparison, and optimization. Next, the 3D modeling, re-rendering, and processing were conducted to finalize the scheme using Rhino or 3DMax to obtain the final schematic design rendering, which is shown in Figure 3. ...
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Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a stud...
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... For example, Kemal et al. [12] used the eye-tracker to obtain objective data on the ship's appearance, including the area of interest (AOI), scan path, and heat maps. Nevertheless, relying on a single physiological signal can lead to one-sided results, and thus, Xiao and Cheng [55] designed several experiments involving eye-tracking, skin conductance, heart rate, and electroencephalography. We notice that physiological signals are more related to the intensity of Kansei than the content; (iv) The final step is constructing the relationship model that links user affections to product parameters. ...
... However, Kansei engineering still has limitations in the data collection process. To capture customers' affective responses towards different products, various traditional methods are widely used, such as user interviews, questionnaires, focus groups, experiments, etc. [10,11,41,55]. We have summarized their advantages and disadvantages in Table 3. labor-intensive; complex; small survey scope Overall, traditional product design methods suffer from several significant disadvantages. ...
As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.
... Furthermore, some studies indicate that, in addition to qualitative vocabulary analysis, physiological indicators can be used to correlate the true emotional state of users in specific scenarios [20]. In this regard, some researchers have proposed using objective emotion measurement techniques, such as eye tracking, skin conductance, heart-rate variability, and brain electrophysiological activity (EEG), as well as combining subjective psychometric techniques, such as the semantic differential method and emotion scales, to determine the perception of industrial robots [21,22]. Finally, there are studies that use both qualitative and quantitative methods in the development of perceptual designs. ...
... The practice of designing industrial robots according to the designerʹs perceptions and creative ideas is always limited and may result in robots with unqualified mechanical performance [21]. Some studies have attempted to address this problem by analyzing the relationship between user perception demands and robot design elements (mechanical structures and components). ...
... (3) As shown in Figure 3, nine out of ten samples (9/10) are distributed to the left of the vertical center line in terms of color features, which suggests that robots with simple color schemes are more appealing to users and create a ʺsimple styleʺ for robots. This result is supported by the literature [21], which suggests that industrial robots, like industrial machinery, should aim for simplicity in their color schemes. We can explain this phenomenon based on the literature findings [32]: the pastel colors represent a relatively plain robot body, which conveys a sense of stillness. ...
How to effectively match the relationship between users' perceptual demands and the characteristics of industrial robot modules becomes a pressing issue when perceptual demands become a significant determinant of whether users purchase and employ industrial robots. In this regard, we propose a Kansei Engineering-based method for industrial robot module configuration, using the module design of a glass substrate transfer robot as an example. First, the method analyzes the perceptual demand characteristics of the target user, utilizing the semantic difference method, and then establishes a mapping relationship between the user's perceptual demand and the robot design elements, utilizing the hierarchical inference method. On the basis of this mapping relationship, the robot module for transfer glass substrates is then designed. Finally, orthogonal design and conjoint analysis were used to effectively and objectively analyze user preferences for various module configuration alternatives. The results indicate that the industrial robot's shape, color, and material are the three appearance characteristics that influence the user's perceptual demands. The slender, rigid design features of the industrial robot, such as the streamlined drive shaft, lengthwise expanded body structure, integrated body structure, and hidden plugs, as well as the simple color scheme and smooth metal surface, are key elements in the industrial robot's perceptual design. The turn shaft module and lift shaft module have respective weights of 35.040% and 31.120%, determining whether the glass substrate transfer robot can create a simple style. In the context of the widespread use of industrial robot modules, the methods and findings of this study offer new ideas for the design of industrial robot modules and broaden the research and applications of Kansei Engineering in module design.
... As the number of digital natives increases and the range of digital design tools/ media expands, the groundwork for a fully digital industrial design process is being laid (Pauliuk et al., 2022;Urgo et al., 2022). Using only the perceptual thinking and creativity of industrial designers, or exaggerating the role of quantitative data in the field of emotional cognition, on the other hand (Venturini, 2022), is a more one-sided approach to industrial design (Xiao and Cheng, 2020). Through emotional interaction with the target user, an industrial design service platform can effectively bridge this contradiction (Schneorson et al., 2019). ...
With the deep integration of industries brought about by big data technology, users’ design needs are diversifying and individuating. Thanks to big data technology, users’ diverse design needs can be precisely met. Meanwhile, big data can be used to realize emotional interaction for personalized design needs of users, resulting in a better user experience. Using grounded theory to mine user demand text data, this paper investigates the influencing factors of emotional interaction and dynamic resource allocation in the digital design supply chain. The results show that government-driven factors have a direct impact on the demand for industrial design in user emotional interactions. Market factors are the most fundamental in the development of an industrial design service platform, and universities play an important role in this. Furthermore, a lack of market sensitivity stems from a lack of emotional interaction with users, resulting in a schism between industry, university, and research, which has become a major impediment to the development of China’s industrial design industry. This study not only lays the theoretical groundwork for understanding the mechanisms of user emotional interaction on IDSPs, but it also points the way forward for future industrial design service platform development.
... Therefore. Xiao and Cheng [54] designed several experiments involving eye-tracking, skin conductance, heart rate, and electroencephalography. We notice that physiological signals are more about Kansei's intensity than content. ...
With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.
Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.
Perceptual image is an important theory often used in product design. However, under normal circumstances, product design does not rely on a single theory and method, but is the result of a combination of multiple considerations. This paper aims to integrate brand identity theory into the perceptual image design method and construct a comprehensive design process, in order to take into account the needs of function, perceptual and product identification. Taking a Chinese brand's smart home gateway product as an example, the cultural elements were obtained through enterprise-oriented desktop research and interview methods. Then, by establishing a sample library of similar products and performing hierarchical statistics, the mainstream design elements of gateway products were obtained as an auxiliary reference. Finally, the gateway products were perceptually measured and then quantitatively analyzed through the Semantic Difference Scale to establish the mapping relationship between image vocabulary and product appearance elements. The cultural elements, mainstream elements and perceptual image elements were summarized from the three dimensions of shape, color and material, and were used in the design practice of smart home gateway products. After inspection, it is determined that the design results meet the design objectives. This study provides a practical, comprehensive and systematic design process for product design based on perceptual image and brand identity.
