Wooden furniture design based on physiological-psychological measurement technology and Kansei engineering: Taking Ming-style chair as an example

Abstract

Psychological and physiological cognitive measurements were combined with Kansei engineering theory to study the shape of the traditional Chinese official-hat chair, a kind of Ming-style chairs, while investigating people’s perceptual cognitive process. This work employed focus groups to obtain 5 groups of representative words and 22 typical samples for evaluation experiments. After data preprocessing, principal component analysis (PCA) was used to extract three main components: “Concise-Ornate”, “Soft-Strong”, and “Elegant-Vulgar”. In terms of component weight calculation, the analytic hierarchy process (AHP) was initially used to calculate the subjective cognitive weight of different components, and then eye-tracking technology was used to assist in calculating the physiological cognitive weight. Finally, combining the subjective and objective weights, the weights of the components were determined as: backrest (41.02%) > stretcher (18.29%) > handrail (11.64%) > top rail (11.14%) > outside stick (8.64%) > inside stick (5.32%) > foot rail (3.95%). The four main components—backrest, stretcher, handrail, and top rail—along with perceptual image evaluations, were selected to establish a multivariate linear regression equation, thereby constructing a mathematical mapping relationship between perceptual image and main design elements. This provides theoretical support for designers in creating different perceptual images.

Full Article

Wooden Furniture Design Based on Physiological-Psychological Measurement Technology and Kansei Engineering: Taking Ming-style Chair as an Example

Yiling Lei, Lei Fu, Ling Zhu and Jiufang Lv *

Psychological and physiological cognitive measurements were combined with Kansei engineering theory to study the shape of the traditional Chinese official-hat chair, a kind of Ming-style chairs, while investigating people’s perceptual cognitive process. This work employed focus groups to obtain 5 groups of representative words and 22 typical samples for evaluation experiments. After data preprocessing, principal component analysis (PCA) was used to extract three main components: “Concise-Ornate”, “Soft-Strong”, and “Elegant-Vulgar”. In terms of component weight calculation, the analytic hierarchy process (AHP) was initially used to calculate the subjective cognitive weight of different components, and then eye-tracking technology was used to assist in calculating the physiological cognitive weight. Finally, combining the subjective and objective weights, the weights of the components were determined as: backrest (41.02%) > stretcher (18.29%) > handrail (11.64%) > top rail (11.14%) > outside stick (8.64%) > inside stick (5.32%) > foot rail (3.95%). The four main components—backrest, stretcher, handrail, and top rail—along with perceptual image evaluations, were selected to establish a multivariate linear regression equation, thereby constructing a mathematical mapping relationship between perceptual image and main design elements. This provides theoretical support for designers in creating different perceptual images.

DOI: 10.15376/biores.19.3.6304-6324

Keywords: Wooden furniture; Kansei engineering; Eye tracking technology; Analytic hierarchy process (AHP); Ming-style furniture

Contact information: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China; *Corresponding author: lvjiufang8189@njfu.edu.cn

INTRODUCTION

The history of human interaction with wood predates the Stone Age. The use of wood in China has a history of thousands of years. Wood products not only provide the material basis for daily life but also carry the spirit and aesthetic ideas formed through societal labor. Through the long history of human interaction with wood, a distinct culture of wood gradually emerged. As a unique symbol within the Chinese cultural system, Chinese wood culture holds significant value. Wood and its exquisite crafts serve as vital carriers and media for disseminating and advancing wood culture, imbued with profound cultural significance and artistic value (Xue and Chen 2024).

In Chinese traditional wood products, furniture—being closely related to human life—embodies the philosophy of Chinese life. The unique craftsmanship of Chinese classical furniture reflects a respect for wood and a reverence for nature. The design and application of these forms possess high artistic and aesthetic value. Ming-style furniture emerged during the development of Chinese furniture, attracting worldwide attention. Gustav Ecke (1896 to 1971), a pioneering scholar in the study of Ming-style furniture, published the first known work on Ming-style furniture, Chinese Domestic Furniture, in 1944. This pioneering work on Ming-style furniture significantly influenced subsequent furniture studies. Ming-style chairs can be categorized by form, such as the official-hat chair, circle chair, and rose chair. Even today, Ming-style furniture occupies a significant position in the Chinese solid wood furniture market.

As a wooden product, the appearance of Ming-style furniture affects consumers’ purchasing decisions. With increasingly diversified and personalized consumer demand, high-end consumer groups tend to customize products according to personal preferences, yet most traditional furniture markets rarely consider these personalized needs. Therefore, it is crucial to design products that conform to consumers’ image preferences (Xiong et al. 2016). In recent years, to explore consumers’ imagery preferences, many designers have employed methods such as the Semantic Differential (SD) method and Analytic Hierarchy Process (AHP) to quantify cognitive data and obtain optimal solutions. However, as mental decision-making involves complex brain processes, the results are not always precise. With technological progress, physiological cognitive measurement technology has become capable of capturing consumer behavior data with high precision. To more intuitively reflect consumers’ implicit behavior, researchers can analyze their cognitive behavior process, aiding the study of decision-making and judgment (Lim et al. 2020). Many scholars have used EEG (electroencephalogram), eye tracking technology, and SCL (skin conductance level) to conduct research on individuals’ physiological signals. Measuring consumers’ physiological data has become central to cognitive research.

Therefore, this paper employs a combination of psychometric and physiological measurements to investigate the psychological and behavioral processes of consumers, and to develop new methodologies for wooden furniture design. By translating consumers’ imagery preferences, this study provides a method for achieving personalized design and addressing the issue of product stacking under the mass production model of traditional furniture enterprises. This approach saves materials and lays the foundation for shortening the new product development cycle, reducing costs, and achieving green, intelligent manufacturing and sustainable development.

LITERATURE REVIEW

Kansei Engineering and Wooden Furniture

The word “Kansei” is derived from the Japanese word for “people’s emotional, perceptual, and sensory responses.” The concept of Kansei Engineering was first proposed by Kenichi Yamamoto in Japan. It is a systematic research method that integrates the emotional and psychological aspects of users into product design and development. It combines the rational analysis of human emotion and engineering to explore the relationship between human perception and object design characteristics. The product image reflects consumers’ cognition of product form, and understanding how consumers evaluate product image shape can better inform design to meet consumer preferences.

Regarding Kansei Engineering, in the context of the side view of a car, Kang and Wang (2022) utilized neural networks, the entropy method, Quality Function Development (QFD), and other techniques to construct a relationship matrix between customers’ aesthetic preferences and products characteristics. Guo et al. (2023) built a model representing the relationship between user perception and interface attributes for individual users. Taking the electric recliner as an example, Zhou et al. (2023) used Kansei Engineering and Grey Relational Analysis (GRA) to explore the image modeling optimization design process based on users’ perceptual preferences. By integrating Kansei Engineering with other methodologies to analyze target products, they have effectively met user needs and achieved successful imagery alignment.

In terms of wooden furniture, An et al. (2022) used the method of Kansei Engineering to study traditional Chinese wooden screens, and applied Quantification Theory Type I for quantitative analysis. Xu and Pan (2023) analyzed and discussed solid wood chairs using Kansei Engineering concluding that this process scientifically and effectively reflects consumers’ potential perceptual needs for the form images of solid wood chairs, thereby improving design efficiency in furniture product development. Zuo et al. (2023) combined perceptual engineering, AHP, and QFD to study the Ming-style chair, resulting in a design solution that satisfies users’ perceptual needs. Lin et al. (2024) used Kansei Engineering to conduct perceptual semantic experiments on Ming-style, Qing-style, and modern Chinese-style furniture, and established mapping models using Quantification Theory Type I.

Currently, few studies have focused on consumers’ perceptual imagery in classical furniture. Literature indicates that studies on Ming-style chairs primarily employ subjective evaluation methods to gather relevant data in the initial stages, followed by model construction for subsequent analysis. These studies lack consideration of consumers’ implicit physiological behaviors and fail to collect objective data for judgment and comprehensive analysis during preliminary data collection.

Physiological and Psychological Measurement Technology

People have corresponding psychological and physiological reactions to stimuli, which can reflect people’s perceptual cognition and preferences to a certain extent.

Psychological measurement technology

In design, subjective measures are based on relevant psychological theories, such as interviews, Semantic Differential (SD), focus groups, Analytic Hierarchy Process (AHP), and other methods that are used to quantify and analyze people’s psychological characteristics and decisions. The psychological cognitive measurement method is a subjective measurement method, which is common in the use of design science. In this study, the SD method and AHP are primarily used to measure psychological cognition. Cui et al. (2022) adopted the Comprehensive Evaluation (CE) method, establishing a ladder fuzzy AHP, and proposing a CE model to evaluate the performance of products. Chen and Sun (2023) explored Research and Development (R&D) strategies for custom kitchen cabinets made from these panels by integrating QFD and AHP methods. Based on the AHP, Liu et al. (2023) made a quantitative analysis of the user needs of dining chairs by combining qualitative and quantitative methods. Miao et al. (2024) adopted AHP, morphological analysis, and QCA methods to study the preference of children’s desk users.

Physiological measurement technology——Eye tracking technology

With advancements in science and technology, individuals can utilize various physiological measurement tools to gather data on subjects’ physiological responses. Through collecting physiological data, one can delineate the progression of a user’s psychological state during the judgment process through objective physiological changes. Advancements in physiological measurement technology have led to various methods, such as eye tracking, event-related potentials (ERP), skin conductance level (SCL), and facial recognition, which can capture and interpret subjects’ physiological indicators, facilitating data analysis. According to literature, there is a growing trend in the field of consumer psychology to explore the underlying cognitive processes behind users’ objective behaviors using physiological measurement techniques like EEG and eye tracking (Zhu and Lv 2023).

Eye movement data most intuitively reflects the user’s visual cognitive behavior. Hsu et al. (2017) conducted eight perceptual evaluations of 16 chairs while tracking their eye movements. Two main factors, valence and arousal, were extracted from the eight perceptual scales, forming a perceptual plane compatible with Russell’s affective ring model. Zhagn and Xu (2020) used the chair of the Tang Dynasty as an example, combining the eye tracking and perceptual evaluation methods to evaluate the semantic acceptability of the chair of the Tang Dynasty, and determined that the backrest, handrail, and chair legs are the most important morphological features in the design of the chair in the Tang Dynasty. Yu et al. (2021) used a combination of eye tracking techniques and subjective assessment to study individual aesthetic preferences in wood color. Li et al. (2022) studied robots with moderate anthropomorphic features, aiming to provide a basis for the objective evaluation of affective impressions. The frequency and duration of gazes can differentiate positive impressions of the appearance of a humanoid robot. Ilhan and Togay (2023) assert that eye tracking technology can serve as a tool to gather implicit aesthetic information from users. Their research on modern sofas aims to ascertain the influence of various product details on user appreciation, which can be interpreted by specific gaze metrics. Miao et al. (2024) employed AHP to analyze behavioral demands and compared hot spot map, track maps, and other data collected during eye movement experiments as children observed different lockers.

In summary, the current application of physiological cognitive measurement has reached a relatively mature stage and has demonstrated effectiveness in the process of product design decision-making, suggesting that physiological cognitive measurement technology is both reliable and feasible. Commonly used indicators of eye tracking technology include the first fixation time, the number of fixations, and the duration of fixations. However, eye movement technology is still less utilized in traditional wooden furniture design. Furthermore, it has not been considered in design weighting for quantitative research. Compared to psychometric technology, using eye-tracking technology to capture subjects’ eye movement behavior is more objective and can assist in psychometric analysis and judgment. Through quantitative analysis and calculation, it can indicate which design components are more important, providing theoretical guidance for subsequent design.

EXPERIMENTAL

Research Framework and Object

This paper used the traditional Chinese classic seat known as the “official-hat chair” as a case study. The official-hat chair is a type of Ming-style chair. The name “official-hat chair” originates from its resemblance to the hats worn by ancient Chinese officials. The technical roadmap is outlined in Fig. 1. The chair’s shape and corresponding name are illustrated in Fig. 2. In terms of furniture modeling classification, the “official-hat chair” falls under the category of “armchair,” which were highly popular during the Ming Dynasty. Therefore, the subsequent discussion will refer directly to “armchair.”

Research Method

AHP

The AHP is a hierarchical weight decision analysis method. A scale method ranging from 1 to 9 points is typically employed for scoring, as demonstrated in Table 1, to assess the relative importance of the two indicators.

Table 1. Scale Method of 1 to 9 Points

Steps of the Analytic Hierarchy Process:

Step 1: Establish a judgment matrix by inviting professors and experts in the field of furniture to provide scores. The parameters I and j are compared pairwise using the 1 to 9 scale method. Each judgment was recorded in the form of a pairwise comparison matrix A of dimension n × n, parameter b_ij was the result of comparing the contribution of parameter b_i and parameter b_j to the previous level. Equation 1 represents the pair-wise comparison matrix denoted as A,

(1)

where was the number of parameters to be compared.

Step 2: Normalize the judgment matrix and calculate the weight values of each item, as shown in Eqs. 2 and 3,

(2)

(3)

where W_i is the weight of the i indicator.

To judge the rationality of the weight value, it is necessary to conduct a consistency test on the judgment matrix.

Step 3: Calculate the maximum characteristic root value of the matrix, and the calculation Eq. 4 is shown as follows:

(4)

Step 4: The consistency index (CI) is calculated by combining λ_max with the Eq. 5:

(5)

Step 5: The consistency ratio (CR) is calculated by Eq. 6, in which the random index (RI) is shown in Table 2. If CR < 0.1, it means that the evaluation matrix consistency test is passed, and the smaller the CR value, the better the matrix consistency.

(6)

Table 2. RI Indicators of Judgment Matrix

The entropy method

The entropy method is a weight allocation technique based on the concept of entropy from information theory, and it is an objective weight calculation method. It calculates the weight of each index based on the amount of information it carries and its degree of variation.

Step 1: Determine whether the data represents positive or negative attributes, and then standardize the data to remove dimensional differences, compressing each indicator to a range between 0 and 1. Positive attribute indicators are normalized according to Eq. 7, while negative attribute indicators are normalized according to Eq. 8,

(7)

(8)

where represents the standardized value, X_ij represents the raw data of the j index of the i component, X_jmax represents the maximum value in index j, and X_jmin represents the minimum value in index j.

Step 2: To ensure the normal operation of subsequent logarithm calculation, a “translation value” is uniformly added to all indicator data, so that the data of this indicator is non-negative. This value is positioned 0.001 in this paper as shown in Eq. 9:

(9)

Step 3: Calculate the proportion of the j sample value of item i according to Eq. 10:

(10)

Step 4: Calculate the information entropy of each index according to Eq. 11,

(11)

where = 1/ln(n).

Step 5: Information utility value d_j according to Eq. 12:

(12)

Step 6: Weight coefficient value according to Eq. 13:

(13)

Step 7: Comprehensive evaluation of the evaluation object according to Eq. 14:

(14)

Quantification theory type I

Quantification Theory Type I employs multiple linear regression analysis to establish a mathematical model. In this context, the perceptual image evaluation serves as the dependent variable, a quantitative variable. The decomposition of furniture styling design elements constitutes the independent variables, which are qualitative variables. Through the analysis of Quantification Theory Type I, the correspondence between user affective needs and design elements is explored, yielding standardized coefficients and determination coefficients for each category of styling design elements. The standardized coefficients of styling design elements further elucidate the impact of each element within the design on affective imagery, while the determination coefficient indicates the precision of the model.

(15)

where is a constant, whose magnitude is determined by the constant of the j-th styling element in the k-th category, represents the error generated when investigating the imagery value of the i-th sample, =1, 2, …, .

The Establishment of Perceptual Image Vocabulary Database

Through extensive collection of furniture literature from books, magazines, and online sources, this study employs Jieba segmentation to extract adjectives and determine the frequency of emotional image words. Among which, the 40 most frequent emotion-related words were selected. To mitigate participant fatigue during the test, the Focus Group method was employed to categorize and refine the collected words. This qualitative research approach enables the exploration of individuals’ attitudes, perceptions and opinions by facilitating open discussions among a group of respondents. Eight focus group members, including furniture experts, designers, and graduate students, were assembled to categorize similar words. After classification, the eight group members selected the perceptual words most suitable for the official-hat chair. Ultimately, five words were identified, and antonyms were found to form the group of perceptual image words: “Concise-Ornate”, “Soft-Strong”, “Ethereal-Thick”, “Smooth-Stagnant”, and “Elegant-Vulgar”.

Table 3. The Form Decomposition of Armchair

The Establishment of Object Sample Repository

A total of 215 armchair samples were collected for this study, and the modeling elements of the armchairs were deconstructed using morphological analysis. Morphological analysis, a non-quantitative research method proposed by Professor Fritz Zwicky, an astrophysicist at the California Institute of Technology, applies morphological theory to guide innovative design. The overall external form of a product is constructed from various product modeling design elements. In this study, all samples were decomposed and classified by morphological elements, and the most representative pattern in each category was selected for the extraction of morphological lines. The form decomposition is illustrated in Table 3. In the subsequent product form description, the codes of form elements will be used.

To obtain typical experimental samples and include as many morphological elements as possible, a total of 22 representative samples were selected for subsequent experiments. All images were desaturated using Adobe Photoshop, an image processing software, to minimize the influence of material and color on shape perception, and convert into images with dimensions of 1680 mm × 1050 mm, with a uniform resolution of 300 dpi. The viewing angle of the armchair sample images was primarily set at 45° to display as many details as possible for the subjects.

Questionnaire Data Collection

An online survey was used to administer the questionnaire, requiring participants to complete it carefully in a quiet and uninterrupted environment. The questionnaire utilized a seven-point Likert scale, where “1” indicates strong agreement with the left perceptual vocabulary, “7” indicates strong agreement with the right perceptual vocabulary, and “4” represents neutrality. In total, 64 questionnaires were collected, and 4 invalid questionnaires were excluded.

To eliminate the impact of differences in the dimensions of various indicators, it is necessary to normalize each indicator’s data by mapping all values to the range [0,1]. The normalization calculation Eq. 16 is as follows,

(16)

where R is the data after standardized processing, and r is the original indicator data.

After analyzing the processed data, the values of each sample for different perceptual words were averaged to obtain the mean value of perceptual image evaluation, as shown in Table 4.

The software SPSS27 was used to perform principal component analysis (PCA) on the data, a statistical method that reduces multiple variables into a small number of principal components that capture most of the information from the original variables. First, the Bartlett sphericity test and the Kaiser-Meyer-Olkin (KMO) test were performed to evaluate the significance and adequacy of the data for factor analysis. The KMO value of the questionnaire data was 0.614, which was greater than the critical value of 0.6, and the Bartlett sphericity test results showed that the P-value was 0.000, which was less than 0.05, indicating a significant difference. Therefore, the data are suitable for PCA.

Table 4. Average Scores of Each Sample Under Different Perceptual Words

The eigenvalues and variance contribution rates of each factor were obtained through PCA using SPSS. Table 5 shows that the first three common factors explained 94.973% of the total variance, indicating they represented 94.973% of the original five indicators measuring perceptual image, implying minimal data loss and effective interpretation of the initial data. Factor rotation was performed using the varimax method; factors with absolute values less than 0.05 are represented by blank spaces, and negative values indicate negative correlations between indicators, as shown in Table 6.

Table 5. Results of PCA

Table 6. Rotated Component Matrix

Three factors were obtained by PCA using SPSS, and the words with the highest loadings for each component were selected for subsequent research: “Concise-Ornate”, “Soft-Strong”, and “Elegant-Vulgar”, representing the decorative factors (Y1), linear factors (Y2), and style factors (Y3) of the armchair.

Psychological Evaluation Weight Calculation

The hierarchical structure model of the armchair was established, and the user judgment matrix was constructed using the Analytic Hierarchy Process (AHP) for the design elements in the middle layer, as shown in Fig. 3. Five experts were invited to score seven indicators in the AHP hierarchy using the 1 to 9 scale method. Then, the geometric mean method was used to aggregate the scores of the five experts into a table, followed by subsequent calculations, as shown in Table 7.

Fig. 3. Hierarchical structure model of the armchair

Table 7. Comprehensive Value of AHP Scores of 5 Experts

Table 8. Consistency Test Value

The eigenvector and weight were calculated according to Eqs. 2 and 3, and then the consistency test of the scoring matrix was performed. The maximum characteristic root value was calculated according to Eq. 4, the CI value was obtained according to Eq. 5, and the CR value was calculated according to Eq. 6. With CR = 0.022 < 0.1, the consistency test results, shown in Table 8, indicate that the consistency test was passed. The weight value obtained was used as the subjective evaluation weight of each component. From this, the mutual importance of each part can be evaluated: Backrest > Stretcher > Top rail > Handrail > Foot rail > Inside Stick > Outside Stick. Among them, the weight of backrest modeling accounted for 34.63%, the highest proportion, indicating that people consider the backrest modeling to be the most important in the cognitive process. The second most important parts are the stretcher and top rail, accounting for 23.24% and 13.51%, respectively, indicating that people also consider these two parts important in the cognitive process. Then came the handrail, foot rail, inside stick, and outside stick, accounting for 10.47%, 7.31%, 6.38%, and 4.46%, respectively.

Physiological Cognitive Experiment

AHP is merely a subjective scoring method for experts to determine the weight of different components, and the selection results do not truly reflect the decisions and feelings of target users. For objective evaluation, physiological indicators, such as eye tracking, can assist in judgment. By analyzing product visual recognition, the weight differences of factors affecting product visual recognition features can be better understood, which is more intuitive than subjective scoring. Additionally, implicit psychological and behavioral information can be inferred from the subjects’ cognitive processes.

Experimental purpose

The eye movement experiment helps to identify which parts the subjects focus on when judging the perceptual image words and which parts are important in the cognitive process of this perceptual phrase.

Experimental materials and procedures

In this experiment, an eye tracker with a sampling rate of 250 Hz and ErgoLAB 3.17.8 were used to collect eye movement data. The experiment was divided into three groups based on the three sets of perceptual words obtained, with a total of six stimuli in each group. The images of each stimulus were arranged in a 2 × 2 layout, and the stimulus contents of each group were used as experimental materials, comprising 22 chair pictures and two spare chair pictures in the questionnaire survey. There was a 1-second gray screen rest time between the stimulus images to allow the eyes to refocus. To facilitate subsequent result processing and analysis, areas of interest (AOIs) were marked for different parts of each chair, and all similar parts were grouped together.

Experimental subjects

In this experiment, 18 graduate students majoring in furniture design, industrial design, or product design were recruited as subjects. All participants had binocular visual acuity or corrected visual acuity above 1.0, and all were right-handed.

Experimental process

Before the experiment, the equipment was adjusted. Each participant sat upright, holding the mouse in their right hand, with the distance between their eyes and the screen being 60 to 65 cm. The sight calibration task was completed before the experiment.

During the experiment, the Concurrent Think Aloud method was adopted. The subjects were asked to judge which of the four chairs in each stimulus was most consistent with the cue word of the perceptual word, and sufficient thinking time (30 s) was provided for each stimulus. After making their judgment, they informed the nearby recorder, then clicked the mouse with their right hand to continue. Then, they moved on to the next stimulus picture and repeated the process until the end of the three sets of experiments, as shown in Fig. 4.

Fig. 4. A set of experimental flow diagrams

RESULTS

Physiological Cognitive Weight Calculation

Eye movement data collection includes numerous indicators. During the judgment process, subjects compare different areas of interest. Therefore, the eye movement indicators shown in Table 9 were selected for subsequent analysis.

Table 9. Meaning of Eye Tracking Indicators

In this experiment, the visit counts and the duration of visits are meaningful for understanding the participants’ preferences for different parts of the armchair. Table 10 displays the proportion of visits to different parts across three sets of affective vocabulary. According to the results of this experiment, for the “Concise-Ornate” pair, participants paid more attention to the backrest and stretcher, as these are the parts of the armchair that are most easily decorated—the less decorated, the more concise. For the “Soft-Strong” pair, participants focused more on the top rail, handrail, and outside stick, as the curves and trajectories of these parts influence people’s judgments of this affective pair. In judging the “Elegant-Vulgar” pair, besides placing more emphasis on the inside stick, other parts were relatively less prominent, indicating a consideration of the overall style. The results of the visit frequency also effectively elucidated the three factors derived from the principal component analysis: the decoration factor, the linear factor, and the style factor. Overall, based on the count of visits, the preference order is Backrest > Stretcher > Outside Stick > Handrail > Top rail > Inside Stick > Foot rail.

Subsequently, the eye-tracking data for each set of vocabulary was processed to obtain the mean values. The data from the three sets were then averaged, resulting in the values presented in Table 11.

Table 10. The Proportion of the VC