The Application of Flow–Clutch States and AHP-QFD-FBS in the Design of Parent–Child Interaction Exercise Bikes

Abstract

Under the pressure of work and family life, parents face sub-health issues and decreasing communication with their children, while children’s physical and mental health declines due to academic stress. Moderate exercise and companionship could enhance fitness and improve parent–child relationships, driving demand for parent–child interactive fitness facilities. This study aimed to propose a design approach for parent–child interactive exercise bikes. Initially, guided by the flow–clutch state theory, which distinguishes the flow state (highly focused and joyful) from the clutch state (any performance increment or exceptional performance that occurs under pressure), we analyzed user requirements in different psychological states. The theory prioritized design features that promote sustained flow and clutch, ensuring that the product meets the dynamic needs of parents and children. Second, the Analytical Hierarchy Process (AHP) quantified the requirements, Quality Function Deployment (QFD) translated them into functions, and the function–behavior–structure (FBS) model defined the product structures. This study integrates the flow–clutch state theory with AHP-QFD-FBS, proposing a scientific and innovative design approach for the successful design of exercise bikes that meet parent–child needs. The product features novel functions and a rational structure, effectively promoting flow and clutch states to enhance parent–child interaction. This research provides a multi-method design case and practical guidance for parent–child products, benefiting family well-being and relationships.

1. Introduction

1.1. Background

With the fast growth of the Chinese economy, many individuals experience pressure from their jobs and family life, such as working extra hours or throughout the night [1], and many present with sub-optimal health [2]. According to the China Association of Chinese Medicine for sub-health status (SHS) clinical guidelines, in accordance with Traditional Chinese Medicine (TCM), SHS is identified among conditions that exhibit signs of vibrancy, bodily activity, and adaptability; however, it cannot be identified as a clinical or sub-clinical disease [3]. If treatment is delayed, the body could progress into an illness state due to such prolonged poor health [4]. Furthermore, more hours spent at work appeared to be linked to poorer parental participation by adults in terms of free time and taking care of kids and, thus, lower the level of parent–child connections [5,6,7]. In nations with strict educational regulations and checks, several studies have found that the burden of academia and pressure on young people might affect their fitness level, habits, and sedentary habits [8,9]. Based on the works of several researchers, sitting for extended periods has a significant impact on the bodily fitness of school-aged boys and girls [10,11]. Moreover, in the unlikely situation of a poor connection between parents and their kids, youngsters believe that they lack encouragement from society and also feel isolated in dealing with their schoolwork [12]. Pupils who have poor relationships with their guardians are more likely to experience scholastic anxiety [13,14,15], the negative effects of sedentary behavior, poorer learning outcomes, and lower general fitness [16]. Furthermore, parent–offspring links and joint involvement in sports may enhance parent–child relationships and the welfare of the family [16]. As a result, there remains a growing need for parent–child interactive exercise facilities, with the assumption that new gym products would improve physical wellness and parent–child relationships. The creative design of parent–child interactive exercise equipment has, therefore, sparked considerable interest.

Scholars have recently investigated the creative design of parent–child interactive products, including interactive gaming furniture design [17], parent–child sports aesthetic experience shopping centers [18], and parent–child interactive toys [19]. Studies on user-specific interactive fitness equipment for parents and kids remain lacking, however. Furthermore, during the creation of parent–child interactive devices, designers cannot provide systematic theoretical advice. Consequently, one of the greatest obstacles that demands addressing is the exploration of systematic design methodologies to improve the scientific accuracy and efficiency of parent–child interactive exercise product design and to create fitness facilities that satisfy user needs.

1.2. Literature Review

The psychological state of the consumer must be understood during the design process for sports products. At present, flow is a highly widely used framework for understanding optimal psychological conditions in a situation of engaging in exercise. The concept of flow was established to explain the usual mental condition that occurs when the individual performs optimally in an assignment while experiencing a sense of fulfillment [20]. Despite its widespread adoption in the sport and exercise literature, the traditional flow model has been challenged by research indicating the possibility of additional ideal psychological states apart from flow. In a typical athletic setting, academics determined another important psychological state: the clutch state [21,22]. The clutch state, which involves participants achieving exceptional outcomes under elevated psychological strain, has been proposed as the psychological state underlying clutch performance [21,22]. An integrated model of flow and clutch states was developed following numerous explorations of sport and exercise [21,22,23], which implies that it is reasonable to integrate flow and clutch states in the study of physical activity. Consequently, the theoretical basis for this study on the design requirements of parent–child interactive fitness facilities is flow and clutch states.

One of the key components of product design studies involves the consideration and choice of design requirements; the AHP belongs to the popular and successful multi-criteria decision-making (MCDM) approach [24]. The benefits of the AHP are its straightforward nature, robust usability, practicality, and systematicity [25,26]. By reducing mistakes in judgment by checking the consistency of decision evaluation and converting qualitative determination variables into quantitative weights through a pairwise comparison, the AHP can make decisions more accurate, logical, and intuitive, making it simpler for ordinary individuals to comprehend, accept, and use [27,28]. These beneficial aspects have seen the AHP gain widespread utilization in the assessment of design indicators and design decisions. Researchers have recently investigated the use of the AHP in the design of several items, including elderly family medical products [29], surgical auxiliary equipment [30], sports earphones [31], and home entrance disinfection devices [32], with the latter study comprehensively proving the applicability and advantages of the AHP in the realm of product design demand indicator evaluation and decision making.

The AHP calculation procedure is much more straightforward, clear, and obvious than other approaches to decision-making, e.g., fuzzy AHP, meaning it is simpler for subsequent designers to comprehend and use to swiftly arrive at efficient design decisions [33]. As a result, the utilization of the traditional AHP technique in this research ensures the scientific validity and efficacy of the design guiding methodology while also improving its usability and applicability. Following the assessment and prioritization of design needs, it is essential to consider how to convert design demands into technical and functional demands for the products and implement these functions with suitable structures. Product conceptual design decisions can be made more reasonable and reliable by using the AHP for quantifying decision issues, which helps to mitigate the subjectivity and inconsistency of decision findings [34]. The AHP was also used to create a hierarchical system of assessment for indoor exercise machines for older people to lessen the influence of subjectivity and bias in the selection of exercise equipment [35]. In addition, using the AHP, researchers evaluated mobile fitness apps on multiple parameters during the COVID-19 pandemic [36]. Although the AHP can quantify the design demands and make clear how important they are to guide design purposes, it cannot define the precise process for converting user demands into product functionality.

A user-centered method of quality management, Quality Function Deployment (QFD), is employed to convert user requirements into particular functional and technological features for products [37]. The QFD technique in fitness footwear manufacturing provides guidance for meeting customer expectations [38], serving as an organized method of developing products that include a thorough and methodical examination of consumer needs to ensure the finished product lived up to customer aspirations. By assessing the demands of users through a quality house model and showing the level of connectivity between user and functional demands, the technique has the potential to quickly and efficiently produce an optimum design solution [39,40]. The QFD methodology can facilitate the organized analysis of functionality and convert design needs into product functions, but it cannot assist in developing and executing product functions. Employing the hierarchical mapping-based function–behavior–structure (FBS) model could generate industrial design plans, provide a mapping mechanism between the functions and structures of products, and explain the precise techniques for achieving product functionalities [41]. Recent research on designing rehabilitation apparatus for seniors according to the FBS framework has been conducted by academics, and its utilization could improve customer satisfaction and offer a fresh approach to fitness design [42].

In recent research on industrial design, academics have investigated the united or separate use of the AHP, QFD, and FBS theoretical techniques. For instance, Hridoy et al. employed the AHP and QFD methodologies to enhance tractor seat design for Bangladeshi tractor drivers, demonstrating the potential usefulness of the AHP-QFD approach in product development design [43]. Ahmad et al. utilized the AHP to design a manual wheelchair for the elderly, considering cost, performance, safety, and maintenance [44]. These studies highlight how the AHP may assist designers in evaluating and selecting the most viable design strategies to make decisions based on criteria and sub-criteria. Using QFD and FBS methodologies, Guo et al. developed a novel hospital guidance robot design that objectively determines user demands and directs design direction [45]. To address household health and safety concerns in the post-pandemic period, researchers have also created home entry disinfection devices by combining AHP and FBS with other techniques [32]. It is clear from the studies highlighted above that the AHP, QFD, and FBS approaches have wide applications in product design. Nevertheless, the AHP-QFD-FBS method has yet to be implemented in the creation of parent–child interactive exercise bikes. Research in sport psychology has demonstrated that the theories of flow and clutch states can provide the best possible experience. In the realm of leisure long-distance running athletics, for example, researchers have assisted athletes in designing their training regimens and optimizing their running experiences by considering the traits relating to the different stages of flow and clutch states, which might impact long-lasting engagement [46]. According to Patrick et al., advanced climbers experienced favorable outcomes from both flow and clutch states, including pleasure (flow states) and emotions of success (clutch states) [47]. It was additionally proposed that exceptional performance in physical activity and sports is the result of these two psychological states [48]. Analyzing the above research, flow and clutch states are commonly utilized in athletics and fitness [46,48] but are seldom discussed in the field of product design.

It may be inferred from the foregoing that guiding parent–child interactive sport product design necessitates flow and clutch states. Design needs are logically evaluated and prioritized using AHP; by breaking down intricate design specifications into discrete functional parts, QFD supports defining the essential features and attributes of an object, and FBS could transform those functions into behaviors and structures that follow an organized logic-based framework. While previous studies have often focused on either psychological theories (such as flow theory) or engineering design methods (such as AHP and FBS), this study uniquely integrates both approaches to address a critical gap in the field. By combining the flow–clutch state theory with AHP-QFD-FBS, our approach not only captures the dynamic psychological needs of users (e.g., timely operational feedback and assessment of stress and challenges) but also systematically translates these needs into functional and structural design solutions. With the theories of flow and clutch states and the AHP-QFD-FBS methodology, the whole procedure, from determining design requirements to demand evaluation, functional and technical characterization, and structure design, proceeds incrementally, guaranteeing that every phase related to the scheme design is grounded in mathematical concepts and plausible basis, thus increasing the viability of the achieved design solution.

1.3. Objectives

The objective of our research was to investigate methods for developing parent–child interactive exercise bikes strategically utilizing flow and clutch state models, as well as AHP, QFD, and FBS approaches. It aimed to help designers better comprehend and satisfy the different demands of consumers for parent–child interactive fitness devices, accordingly improving both the quality and practicality of design strategies. Furthermore, this study gives logical and application-oriented guidelines for future developers, expanding the subject of parent–child interactive sports product design.

1.4. Significance

This study proposes an innovative design approach for parent–child interactive exercise bikes by integrating the flow–clutch state theory with the AHP-QFD-FBS method, offering significant theoretical and practical value. Theoretically, it addresses the research gap in combining psychological theories with engineering design methods, expands the application scope of the flow–clutch state theory, and provides a new framework for multi-method integrated product design. Practically speaking, by capturing users’ dynamic needs, the study design features promote sustained engagement and adaptability, significantly enhancing the user experience and parent–child interaction quality. Additionally, this research provides methodological support for industry innovation in parent–child interactive products, meets the market demand for high-quality parent–child fitness facilities, and promotes the adoption of healthy lifestyles and social harmony by enhancing family well-being and happiness.

2. Methods

2.1. Analytic Hierarchy Process (AHP)

Professor Saaty from the United States developed a method for making decisions called the Analytic Hierarchy Process (AHP) [26,28]. The AHP provides a methodical, adaptable, and even scientific means to assess complicated issues using both quantitative computations and qualitative analysis. Analyzing multiple-criteria judgment issues methodically, evaluating and quantifying the significance regarding those criteria, and identifying essential decision-making markers with weight computation and prioritization are the main components of the AHP [26].

2.2. Quality Function Deployment (QFD)

Quality Function Deployment (QFD) is a methodical strategy in product development that helps firms turn design needs into specific product functions and technical characteristics, guaranteeing the finished output meets consumer requirements [49]. The main component of QFD is the multidimensional matrix known as the House of Quality, which highlights and analyzes the relationship between product functions and design demands to ensure the end product meets customer-desired outcomes [49,50]. When evaluating the functions adopting the QFD strategy, the quality house must initially obtain the design needs and weights following the AHP.

2.3. Function–Behavior–Structure (FBS) Model

Originally proposed by Gero [51], the function–behavior–structure (FBS) paradigm is an approach to designing innovative products. Function, behavior, and structure are mapped to create the FBS framework, a design expression paradigm that improves the relationship among functions, behaviors, and structures [52]. Function, in the FBS framework, refers to the primary goal of industrial product design, which is to satisfy user requirements; behavior explains how the artificial mechanism accomplishes its purpose and how individuals interact with the goods; structure acts as a conduit for behavior, forming the product and connecting different functional modules [52]. The hierarchical extension of product functions may be performed via the FBS mapping method, and the behavioral elements for functional application can deduced, allowing the theoretical structures of the device to easily be established.

2.4. Fuzzy Comprehensive Evaluation (FCE)

The fuzzy comprehensive evaluation methodology belongs to a quantitative-based assessment approach that is primarily employed to address assessment troubles involving uncertainty and ambiguity [53]. It is marked by clear and systematic results and is more competent at addressing complex and difficult-to-quantify difficulties by transforming a qualitative evaluation into a quantitative analysis [53,54].

As a portion of the FCE, the assessor primarily determines the degree of affiliation for every analysis item for each assessment level applying the specific findings of each indicator, and this process creates the fuzzy relationship matrix [54]. Design demands, which are used as a measurement object of the product design, are combined with the weights obtained using the AHP to create the weight vector [55]. Merging the fuzzy evaluation matrix and weight vector, a fuzzy comprehensive assessment was performed to ascertain the extent to which every design scheme complied with the needs of consumers and users [55], which facilitated a full analysis of the design project.

3. Product Design Process Framework Based on Flow–Clutch States and AHP-QFD-FBS

No clinical, animal, human tissue, or biological sample-related experimentation was utilized within this study; instead, it concentrates on product design approaches. Only the experts who took part in the scoring and user needs analysis were human participants in this research; all the panel members were adult volunteers. Under the voluntary engagement premise of the project, the experts were gathered for scoring; before this study started, the researchers had received written agreement from all professionals and had explained what was being studied, the interview procedure, how interview data would be used, and the rights of individuals. This research also has been approved by the Research Ethics and Science and Technology Safety Committee of Hubei University of Technology.

Detailed below are the four primary phases of utilizing flow and clutch states and the AHP-QFD-FBS method for producing the idea architecture:

3.1. User Requirements Analysis Based on Flow and Clutch States

This involves the in-depth analysis and summarization of design demands based on the condition, context, outcome, and phase characteristics of the experience in which the flow and clutch states exist.

3.2. Prioritizing and Quantifying Need Indicators

Consistency tests are conducted, and judgment matrices are created using the AHP. The weights and priority rankings of each hierarchical indicator are then established using SPSSAU for data analysis.

3.2.1. Construction and Calculation of Judgment Matrices

Specialists were requested to apply the “nine-point scale method” for comparing and rating the indicators in the hierarchical model pairwise, thus constructing a judgment matrix $H$:

$$H={\left(h_{ij}\right)}_{n\times n}$$

(1)

In the formula, $i,j=1,2,3,\dots ,n$, where $n$ indicates the order of the matrix; $h_{ij}$ indicates the element of the $i$th row and $j$th column of the matrix, where $h_{ij}=1/h_{ij}$.

The product of the elements of each row in the judgment matrix with the results of the comparison with the other factors is represented by $M_i$:

$$M_i={\displaystyle \prod _{j=1}^n{h}_{ij}}\left(i=1,2,3,\dots ,n\right)$$

(2)

Calculate the geometric mean of the factors in each row of the judgment matrix $h_i$:

$$h_i=\sqrt[n]{M_i}$$

(3)

Normalize the calculation results to find the relative weight values $W_i$:

$$W_i={\displaystyle \frac{h_i}{{\displaystyle \sum _{i=1}^n{h}_i}}}$$

(4)

3.2.2. Consistency Test

Calculate the maximum eigenvalue of the judgment matrix:

$${\lambda }_{\max }={\displaystyle \sum _{i=1}^n\frac{{\left(HW\right)}_i}{n{W}_i}}$$

(5)

In the formula, $n$ is the number of orders of the judgment matrix, and ${\left(HW\right)}_i$ represents the $i$th element of the vector $HW$.

Obtain the consistency index:

$$CI={\displaystyle \frac{{\lambda }_{\max }-n}{n-1}}$$

(6)

Calculate the consistency index:

$$CR={\displaystyle \frac{CI}{RI}}$$

(7)

In the formula, $RI$ is the random consistency index, and the $RI$ values of different order matrices are displayed in

Table 1. Value of matrix order 3–10.

n	3	4	5	6	7	8	9	10
$RI$	0.52	0.89	1.12	1.26	1.36	1.41	1.46	1.49

.

$RI$ The judgment matrix consistency tests are passed when $CR<0.1$. If not, the consistency tests must be repeated until the results meet the requirements, and the judgment matrices must be readjusted.

3.2.3. Calculate the Comprehensive Weights of the Demand Indicators $Y$

$$Y=K_i\times G_j$$

(8)

In the formula $ij=1,2,3,\dots ,n$, $K_i$ represents the weights of the $j$th criteria layer; $G_j$ represents the weights of the $j$th sub-criteria layer.

Determine the primary and subsidiary requirements to direct a further analysis by prioritizing the requirement indicator significance according to the comprehensive weights of each indicator.

3.2.4. Translation of User Requirements into Technical Characteristics

Following the usage of the AHP to determine the weights of the criteria, design aspects must be examined using QFD, which is centered on building quality houses. Through QFD, the association between user demands and the technical features of the product is quantified and visualized. The absolute and relative weight values of the technical characteristics are then obtained to determine the priority and weight relationship between all the different design demands and obtain the crucial design requirements, which are then converted into the function library of the parent–child interactive fitness facility.

Using the quality house scoring results and the overall user need weights, the absolute weights of technical features are calculated ($W_j$):

$$W_j={\displaystyle \sum _{i=1}^n{W}_i}{R}_{ij}$$

(9)

In the formula, $W_j$ represents the absolute weight value of the $j$ technical characteristic and $W_i$ represents the $i$th user requirement weight; $R_{ij}$ represents the strength value of the correlation between the $i$th user demand and the $j$th technical characteristic.

The design requirement importance is normalized to obtain the relative weight coefficients $Y_j$ of the $j$th technical characteristic.

$$Y_j={\displaystyle \frac{W_j}{{\displaystyle \sum _{i=1}^n{W}_j}}}$$

(10)

3.3. Derive Structure from Function

The next step is to import the QFD-derived functional elements into the FBS model for layer-by-layer mapping. This procedure yields the theoretical framework of the item as well as the behavioral variables of its function realization. Finally, the output of novel solutions and the thorough design are complete.

3.4. Design Program Evaluation

The subjective nature of evaluating product design programs led us to choose the fuzzy comprehensive assessment approach to validate their rationality and logic.

By considering design requirements for breaking down product functions, mapping and converting them into device structures, producing conceptual solutions, and assessing the design program, the sequential utilization of flow and clutch states, AHP, QFD, FBS, and FCE facilitates a methodical advancement framework throughout the whole conceptual design routine. This creates an entire product concept design approach procedure, as shown in Figure 1. In addition to offering designers a methodical approach to problem-solving and analysis, this comprehensive process considers the relationships and collaborations among multiple phases in the conceptualization procedures, allowing designers to comprehend their objectives and create parent–child interactive fitness facilities that better satisfy customer needs.

4. Results: Example Design of Parent–Child Interactive Exercise Bike

4.1. User Requirements Guided by Flow and Clutch States

The profoundly joyful, intrinsically satisfying psychological state known as “flow” occurs when a person is fully engaged in a task, ignoring unimportant feelings or ideas, and feels as though everything is falling into place [56]. Research shows that a person can experience several ideal mental states when exercising and participating in sports, even throughout just one activity. A review of event-focused data revealed that participants experienced another, different “clutch” condition besides the flow. Clutch states refer to the mental states that underlie clutch performance [57], which is characterized as improved or superior execution under stress [21]. The integrated model of flow and clutch states has been the most extensively researched among such flow replacements. The integrated model considers the contextual circumstances, the procedure for happening (i.e., the procedures through which, it is suggested, antecedents merge to generate the state), characteristics of experiences (i.e., aspects of being in flow and clutch states), and consequences of both states.

Enabling individuals to achieve a positive state is the goal of flow and clutch generation; the design of the parent–child interactive exercise bike is focused on achieving the ideal experience for users. It is possible to enhance the emotion of happiness of users by incorporating flow and clutch states into the design of fitness bikes and enhancing their flow and clutch states. Flow and clutch antecedents should be adjusted as much as is feasible to maximize the experience because the results of these states are often favorable and individual-specific in workout situations. Considering the prerequisites for the creation of flow and clutch states, we then implement these components to improve the user experience, and the design needs for various phases of parent–child interactive exercise facilities are shown in Figure 2.

While participants stated that both states might be experienced during the same exercise, with transitions between the two states feasible, flow and clutch states are separate and cannot be enjoyed simultaneously [48]. The contextual settings, procedures of occurrence, experiential features, and results of the two states are all described via the integrated model.

Setting open target activities, striking a balance between challenge level and individual skill, and offering effective operational feedback are all necessary in the antecedents of flow experiences. Effortless concentration, simple or elevated feelings related to endeavor, the nonexistence of adverse thinking, the enjoyment of the experience when it is providing a sense of control, positive feedback regarding development (e.g., that the task at hand is proceeding perfectly), and optimal arousal are some of the features unique to the flow stage (e.g., wishing for the situation or action to persist). The fundamental objective of design is to supply individuals with a pleasurable workout.

Under stress, people experience clutch states brought on by processing input relating to the situation, which results in an assessment of the challenges, before deciding to raise the level of effort and intensity to achieve those certain objectives. Clutch states are characterized by the following: intense effort, analytic thoughts, and heightened consciousness. This relates to feedback concerning the objective (e.g., what is left in reaching the specific objectives), motivation for achieving the targets along with attaining the envisioned results regarding the activity, higher levels of arousal (i.e., instead of remaining relaxed, similar to flow), and feelings of attempting to take control (but not absolutely being in control).

Clutch states and flow states have shared characteristics. Individuals in these two stages express absorption during their sport, self-confidence, changed senses (e.g., changed feelings about time), and automatic skills operation. Some results are also shared by flow and clutch states. In both states, those who train and compete for experience reported outstanding performance (i.e., regardless of the target result, individuals appear convinced that they have performed well) and self-reward (i.e., feelings of accomplishment, glee, and contentment). However, while clutch states appear to feel draining, flow is said to deliver a revitalizing feeling [48].

In addition, the flow–clutch state theory could guide the design of the parent–child interactive exercise bike by addressing the psychological needs of users in different states. The flow state, characterized by deep focus and enjoyment [45], was translated into features such as interactive gaming modules. These elements promote sustained engagement by balancing task difficulty with user ability and providing intrinsic rewards. The clutch state, which involves heightened awareness, intense effort, and the automaticity of skills [44], was addressed through effort guidance and real-time feedback mechanisms. These features allow users to adjust their workout intensity and receive performance cues, ensuring they can adapt to changing demands without losing engagement. By integrating these psychological principles into the design, the exercise bike effectively supports both flow and clutch states, enhancing the overall user experience and parent–child interaction. Compared to the conventional concept of flow, the integrated model therefore allows designers to create parent–child interactive sports facilities in a more precise, comprehensive, and sequential fashion based on the ideal experience, thereby assisting individuals in maintaining exercise.

4.2. Analysis of Parent–Child Interactive Exercise Bike User Requirements Using AHP

4.2.1. Identification of Indicators

Based on the user needs for parent–child interactive exercise bikes at various stages of flow and clutch states, it was decided to expand and develop 17 design indicators for assessment metrics. The parent–child fitness bike was investigated using research on flow and clutch state theories, and three layers were categorized via hierarchical generalization: the target layer, the criterion layer, and the sub-criterion layer. The assessment system framework for the parent–child interactive exercise bike was created based on the needs of the three levels, as illustrated in Figure 3. The target layer is the parent–child interactive exercise bike indicator evaluation system (P); the criterion layer comprises structure and style (A), interactive experience (B), performance requirements (C), and emotional belonging (D); the sub-criterion layer comprises a total of 17 indicators from A₁ to D₃.

4.2.2. Constructing Judgment Matrices

To efficiently evaluate the significance and rating of the indicators. Four professors with expertise in industrial design and two engineers with over 10 years of product design experience constituted the expert team. Professors may offer expert judgments and recommendations on the technological viability, human–machine interface design, and applicability of product design tactics since they have specialized expertise and background knowledge. Professional engineers likewise possess a thorough awareness of the market and specialized knowledge of customer demands and requirements. From a distinct vocational standpoint, they could themselves benefit from this research through a deeper comprehension of customers’ needs for parent–child interactive exercise bikes. The experts participating in the AHP evaluation underwent comprehensive training to ensure they held a clear understanding of the methodology and evaluation criteria. The training included an overview of the AHP principles, guidelines for pairwise comparisons, and examples to illustrate the scoring process. This preparation helped minimize inconsistencies in expert judgments and ensured the reliability of the results. The involvement of the aforementioned experts was aimed to guarantee the reliability of the study conclusions and offer a multifaceted perspective on the design requirements of parent–child interactive exercise bikes.

The team of experts compared the indicators within the same layer pairwise using the 1–9 proportional scale approach (as indicated in

Table 2. Judgment matrix index importance level numerical scale table. Source: own processing.

Scale Value	Importance Level	Implication
1	Equally important	Indicator I is of equal importance compared to Indicator J
3	Slightly important	Indicator I is slightly more important compared to Indicator J
5	Obviously important	Indicator I is obviously important compared to Indicator J
7	Significantly important	Indicator I is significantly important compared to Indicator J
9	Absolutely important	Indicator I is absolutely important compared to Indicator J
2, 4, 6, 8	Intermediate value	The importance level is between two adjacent levels
1/2, 1/3, … 1/9	Reverse comparison	If the importance scale of indicator A over indicator B is “n”, the reverse comparison is “1/n”

) and created matrices to evaluate the significance of the linkages between the indicators. Utilizing Formula (4), we determined the weight values of each indication after integrating the core findings of the expert team, and the outcomes are displayed in

Table 3. Target layer judgment matrix and weights. Source: own processing.

P	A	B	C	D	Weights
A	1.0000	1.0890	1.7420	2.1820	0.3392
B	0.9184	1.0000	1.5710	2.0400	0.3115
C	0.5740	0.6366	1.0000	1.1300	0.1906
D	0.4582	0.4901	0.8849	1.0000	0.1588

,

Table 4. Judgment matrix and weights for structure and style criteria. Source: own processing.

A	A1	A2	A3	A4	Weights
A1	1.0000	2.6680	1.6980	1.6160	0.3910
A2	0.3748	1.0000	0.7340	0.9190	0.1685
A3	0.5889	1.3624	1.0000	0.7940	0.2124
A4	0.6190	1.0885	1.2597	1.0000	0.2282

,

Table 5. Judgment matrix and weights for interactive experience criteria. Source: own processing.

B	B1	B2	B3	B4	B5	Weights
B1	1.0000	3.2380	3.0920	0.8360	1.7110	0.3099
B2	0.3088	1.0000	1.2140	0.4260	0.9530	0.1249
B3	0.3234	0.8236	1.0000	0.3260	0.5890	0.1004
B4	1.1957	2.3486	3.0671	1.0000	1.0760	0.2840
B5	0.5845	1.0488	1.6981	0.9296	1.0000	0.1808

,

Table 6. Judgment matrix and weights for performance requirements criteria. Source: own processing.

C	C1	C2	C3	C4	C5	Weights
C1	1.0000	3.6320	1.4680	2.4810	1.5880	0.3299
C2	0.2753	1.0000	0.4470	0.8490	0.3410	0.0921
C3	0.6813	2.2361	1.0000	2.5880	1.1780	0.2440
C4	0.4031	1.1774	0.3864	1.0000	0.5670	0.1141
C5	0.6298	2.9356	0.8490	1.7627	1.0000	0.2200

and

Table 7. Judgment matrix and weights for emotional belonging criteria. Source: own processing.

D	D1	D2	D3	Weights
D1	1.000	1.497	1.000	0.374
D2	0.668	1.000	0.648	0.248
D3	1.000	1.543	1.000	0.378

.

4.2.3. Consistency Test

After the judgment matrices were constructed, consistency examinations were performed to confirm that the panel of experts who participated in the assessment had not made any logical mistakes.

Table 8. Consistency test results. Source: own processing.

	P	A	B	C	D
${\lambda }_{max}$	4.002	4.027	5.081	5.031	3.000
$CI$	0.001	0.009	0.020	0.008	0.000
$RI$	0.890	0.890	1.120	1.120	0.520
$CR$	0.001	0.010	0.018	0.007	0.000

displays the outcomes of the consistency test, which were computed using Formulas (5)–(7). $CR<0.1$ for each judgment matrix, demonstrating the effectiveness of the weight calculation findings and the passing of the consistency tests.

The combined weight value Y of the user needs hierarchy was determined using Formula (8), and the output was thoroughly sorted. The outcomes of the user requirements and detailed weight value computation are shown in

Table 9. The comprehensive weights and importance ranking of indicators. Source: own processing.

Criterion Layer	Weights	Sub-Criterion Layer	Weights	Comprehensive Weights	Ranking
A	0.3392	A1	0.3910	0.1326	1
		A2	0.1685	0.0572	9
		A3	0.2124	0.0720	5
		A4	0.2282	0.0774	4
B	0.3115	B1	0.3099	0.0965	2
		B2	0.1249	0.0389	14
		B3	0.1004	0.0313	15
		B4	0.2840	0.0885	3
		B5	0.1808	0.0563	10
C	0.1906	C1	0.3299	0.0629	6
		C2	0.0921	0.0176	17
		C3	0.2440	0.0465	11
		C4	0.1141	0.0218	16
		C5	0.2200	0.0419	12
D	0.1588	D1	0.3743	0.0594	8
		D2	0.2475	0.0393	13
		D3	0.3781	0.0600	7

. The small differences in weight values were carefully considered during the design process. For example, when two criteria had very close weights, we prioritized the one that aligned more closely with user needs and design objectives, which ensured that the final design decisions were both scientifically sound and user-centered [32].

4.2.4. Results of User Requirements Analysis

The analysis outcomes are in Table 3, Table 4, Table 5, Table 6 and Table 7 and Table 9. The small differences in weight values were carefully considered during the design process. For example, when two criteria had very close weights, we prioritized the one that aligned more closely with user needs and design objectives, which ensured that the final design decisions were both scientifically sound and user-centered [32].

Table 9 shows that the assessment indicators with the highest weight values in the criterion layer are structure and styling (0.3392), followed by interactive experience (0.3115), performance requirements (0.1906), and emotional requirements (0.1588). The primary requirement indicators for the parent–child interactive exercise bikes, as determined via the thorough weight ranking of the sub-criterion layer indicators, are parents and children working out at the same time (0.1326) > timely feedback (0.0965) > motion correction (0.0885) > reasonable structure (0.0774) > space-saving (0.0720) > safety of use (0.0629). The findings of this investigation align with the emphasis of previously published studies on parent–child interactive products, and the creation of such products should prioritize parent–child interaction while guaranteeing its security during use and offering prompt feedback. Parents and kids working out together ought to constitute the primary focus of parent–child interactive exercise bikes, with specific care and attention paid to prompt feedback, motion correction, acceptable structure, space-saving, and user safety.

4.3. Identifying the Functional System of Parent–Child Interactive Exercise Bike

4.3.1. User Requirements Transformed into Technical Characteristics Based on QFD

After employing the AHP methodology to calculate the weight of every design requirement for the parent–child interactive exercise bike, the weights could be applied as a valuable numerical reference to assess the relationship between the design requirements and technical characteristics, resulting in rankings for each technical characteristic. The technical characteristics derived from this study were then scored, whereby, the greater the score, the better the technical feature fit the needs of the consumers and increased their feeling of satisfaction. The design needs were transformed into technical characteristics using QFD theory, and the level of connection between the demands and the technical characteristics matrix was determined via a correlation analysis. All of this enabled the optimal features to meet the needs of parents and children for assessment in the following phases.

To confirm the technical qualities were applicable, the panel of specialists performed a correlation assessment. The team then settled on the technical characteristics of the parent–child interactive exercise bikes that would meet the principal needs of both parents and children.

The QFD technique allowed us to transform the numerous user request indicators into the appropriate product technical characteristics, as indicated in

Table 10. Summary of user requirements translated into technical characteristics. Source: own processing.

Types of User Requirements	User Requirements	Technical Characteristics
A	A1	Ergonomics
		Double position structure
	A2	Appearance design
	A3	Folding and moving structure
	A4	Ergonomics
B	B1	Acquisition of motion data
	B2	Intelligent software control
	B3	Audio–visual and speaking systems
	B4	Acquisition of motion data
		Intelligent guidance interface
	B5	Parent–child interaction
		Intelligent software control
		Virtual reality scenario
C	C1	Ergonomics
	C2	Intelligent software control
	C3	Acquisition of motion data
	C4	Intelligent guidance interface
	C5	Intelligent software control
D	D1	Parent–child interaction design
	D2	Intelligent guidance interface
		Acquisition of motion data
	D3	Intelligent guidance interface
		Acquisition of motion data

, and then evaluate, arrange, and summarize them, as shown in

Table 11. Summary of product technical characteristics. Source: own processing.

Serial Number	Technical Characteristics
T1	Ergonomics
T2	Appearance design
T3	Folding and moving structure
T4	Acquisition of motion data
T5	Intelligent software control
T6	Audio–visual and speaking systems
T7	Parent–child interaction design
T8	Intelligent guidance interface
T9	Virtual reality scenario

.

4.3.2. HOQ Construction Process

The technical characteristics were imported into the HOQ as the ceiling; the user requirements and the comprehensive weights of the user requirements were then imported into the quality house model, which was used as the left wall of the model; scoring was performed according to the correlation between user requirements and the technical characteristics of the parent–child interactive exercise bike, which was applied as the room of the quality house. The correlation relationships are represented by “▣”, “◎”, and “◯”; the corresponding values are “▣” = 5, “◎” = 3, and “◯” = 1, and blank indicates “0” [35,36,37]. The corresponding relationships are highly correlated, generally correlated, weakly correlated, and not correlated, respectively, as shown in

Table 12. Quality house model score assessment summary. Source: own processing.

User Requirement	Technical Characteristics
Requirement	Weights	T1	T2	T3	T4	T5	T6	T7	T8	T9
P11	0.1326	▣	◎	◎	◎	◯		▣		◎
P12	0.0572	◯	▣	◎	◎			▣
P13	0.0720	◯	▣	▣	▣
P14	0.0774	◎	◎	▣	◎			◎
P21	0.0965					◎	▣	◯	◎
P22	0.0389	▣		▣	▣	▣	▣	◎	◎	◎
P23	0.0313	◯	◎				▣	◯	▣	▣
P24	0.0885	◯					▣	◎	▣
P25	0.0563		◎	◎		◯	◎	▣	◎	◯
P31	0.0629	▣	▣		▣
P32	0.0176					▣	◯	◎		◎
P33	0.0465						▣		▣
P34	0.0218						▣		◎
P35	0.0419					◎	◯	◯
P41	0.0594		◎	◎		◎	◯	▣		◎
P42	0.0393		◯				◎		◎	◎
P43	0.0600		◯				◎		◎	◎
$W_j$ (×100)		154	142	186	167	106	220	236	177	126
$Y_j$ (%)		10.14	9.37	12.27	11.03	7.03	14.55	15.61	11.69	8.30
Ranking		6	7	3	5	9	2	1	4	8

Note: “▣” = 5, “◎” = 3, and “◯” = 1.

. The absolute weights $W_j$ and relative weights $Y_j$ of technical characteristics are calculated according to Formulas (9) and (10), and the results are shown in Table 12.

$W_j$$Y_j$ According to the determination of the quality house of both relative and absolute weights, the top six design requirements, in descending order, are parent–child interaction design (T₇), audio–visual and speaking systems (T₆), folding and moving structure (T₃), intelligent guidance interface (T₈), acquisition of motion data (T₄), and ergonomics (T₁). The relative weight share of these six design requirements totaled 75.29%, which is a majority share of the overall design requirements, and other design demands can be integrated into these key design demands. In addition, the parent–child interaction design (T₇) has a separate weight of 15.61% and is a key design item.

4.4. Identifying the Functional Structure of Parent–Child Interactive Exercise Bike

Appearance design falls under outside design and does not fulfill the conditions for participation in the function–behavior–structure mapping; as a result, the demand indication is excluded to direct the appearance design of the product. The functions are accomplished through behavior, and the structure serves as a means to that end. After the above quality house matrix operation, the design focus requirements are obtained as parent–child interaction design (T₇), audio–visual system (T₆), folding and moving structure (T₃), intelligent guidance interface (T₈), acquisition of motion data (T₄), and ergonomics (T₁), which are categorized as the parent–child interaction function (F₁), audiovisual and speaking function (F₂), folding and moving function (F₃), intelligent guidance function (F₄), data acquisition function (F₅), and ergonomics (F₆), which are mapped hierarchically as functional elements in the FBS model, as displayed in Figure 4.

Figure 4 illustrates the transition from functions to structures, highlighting the intermediate behaviors that define how each function is realized. For example, the function “parent–child interaction (F₁)” is realized through behaviors, such as “cooperative sports” and “competitive sports”, which are then mapped to specific structural components like the “sports games” and “double-position structure”. In addition, not all functions correspond directly to structures; some functions require multiple behaviors to be fully realized. For instance, the function “audio–visual and speaking” involves behaviors such as “voice interaction”, “sports data viewing”, “resistance adjustment”, “emergency stop”, and “corrective action”, which are implemented through structural components like “voice module (S₃)”, “WiFi or Bluetooth (S₄)”, “smart display (S₅)”, and “intelligent guidance interface (S₆)”.

4.4.1. Function–Behavior Mapping

The exercise bike has the function of parent–child interaction, which may be mapped to cooperative and competitive sports behaviors. The audio–visual function may be mapped to voice interaction, sports data viewing, resistance adjustment, emergency stop, and corrective action. To enhance space efficiency, the folding and moving features may be mapped in the same manner as retracting the handles, retracting the screens, retracting the chairs, retracting the bracket, retracting the foot pedals, and moving the exercise bike. The intelligent guidance function is accomplished by corrective action, exercise difficulty guidance, attention and effort guidance, and immersion experience guidance. The data-acquiring function is performed by actual motion engagement, which may be mapped to stamp and tread, as well as handle holding behaviors. Ergonomics is a key concern in product design, and the ergonomics features of the parent–child interactive exercise bikes are mostly linked to the behavior of gripping the handle and getting on the chair.

4.4.2. Behavior–Structure Mapping

The aforementioned function–behavior transformation expression is used to further transform the fine-grained user behaviors into structural modules or components that perform these functions, a total of 17 key design structures (S₁–S₁₇) were obtained: cooperative and competitive sports in parent–child interaction can be mapped to sports games and double-position structures; voice modules and WiFi or Bluetooth modules can be implemented for mapping voice interaction; smart displays are used for sports data viewing, resistance adjustment, and emergency stop operation behavior; an intelligent guidance interface represents the mapping result of resistance adjustment, corrective action, exercise difficulty guidance, attention guidance, guiding efforts, and guiding immersion experience. In folding and moving the exercise bike, retracting the handles, retracting the screen, retracting the chair, retracting the bracket, retracting the foot pedals, and moving the exercise bike behaviors are mapped to foldable handles, foldable displays, foldable chairs, foldable bracket, foldable foot pedals, and the universal wheel structural module, respectively. The speed sensor and pedal frequency sensor can be used to represent stamping and treading behavior. By mapping the “holding the handle” and “getting on the chair” behavior, the heart rate sensor and multifunctional handle can be mapped to an adjustable structure for the seat position.

4.5. Scheme Design of Parent–Child Interactive Exercise Bike

By analyzing the flow and clutch state and applying the AHP-QFD-FBS approach, the key structures of the parent–child interactive exercise bike were systematically examined and determined in this research. The parent–child interaction function of the fitness bike adopts the exercise game and two-person position structure, and the double position structure satisfies both parents and children to perform fitness and interact at the same time, and the parent–child interactive fitness status is demonstrated in Figure 5.

The audio–visual speaking function is realized by installing the voice module component, WiFi or Bluetooth modules, smart displays, and an intelligent guidance interface; in addition, the smart display and intelligent guidance interface module also enhance the human–machine interactive experience. We, therefore, focused on designing the interface of the smart display, and, to enable users to view the exercise information remotely, we also designed the cell phone interface. By considering the functional architecture, it is concluded that the smart display section consists of six basic modules: homepage, sports mode, sports process, exercise courses, sports data, and community. The mobile phone section comprises six prime modules: connection, account, sports record, community, “My”, and health status (Figure 6). The smart displays primarily display exercise data recording, parent–child exercise games, and community sports information, as displayed in Figure 7. The mobile phone interface mostly contains the health status and exercise level of the family members, as seen in Figure 8.

Foldable handles, foldable screens, foldable chairs, foldable brackets, and universal wheel modules are convenient for storing the device, and they greatly increase the utilization of space and the use and storage of exercise bikes, as is apparent in Figure 9. The data acquisition function is realized by installing a speed sensor, pedaling frequency sensor, and heart rate sensor; in terms of ergonomics, it is realized via multifunctional handles and by adjusting the position of the chair. The multifunctional handles can satisfy the two scenarios of standing and flat-road riding, and adjusting the seating position can increase the sense of experience (position-adjustable chair and multi-function handle are portrayed in Figure 10).

We have included 3D renderings of the parent–child interactive exercise bike from multiple angles and its storage process to help users better understand the usage flow and structure of the bike. The specific operation process is illustrated in Figure 11.

In addition, we have included designs for the appearance of the parent–child exercise bike, as shown in Figure 12. The form of this product is mainly designed around function and structure, and the overall shape is childish and elegant, satisfying the aesthetics of both parents and children at the same time. In terms of color scheme, the main color is white with dark gray for structural components and blue as an accent color; the overall color scheme references the style of technological products, and these color schemes are not only impressive but also stimulate the imagination and desire to explore.

During the design process, we encountered several trade-offs and limitations. Balancing space-saving and safety was a significant challenge, and, to address this, we opted for a foldable design that minimizes space usage while incorporating reinforced safety features, such as foldable and non-slip pedals. This approach ensures that the bike is both compact and safe for users. Another trade-off involved functional diversity versus user-friendliness, such that, while the diversity of functions can enhance the functionality and interactivity of the device, it may also lead to difficulties in operation, especially for children and parents unfamiliar with technology. Simplifying the user interface can therefore ensure that complex features are easy to operate, thereby achieving a balance between functional diversity and user-friendliness.

4.6. Scheme Evaluation

The panel of specialists who participated in the demand-gathering stage were requested to assess the design scheme to confirm its viability using the fuzzy comprehensive assessment method. The precise procedures are detailed below:

(1)

Use the criteria layer indicators from the hierarchical model in Figure 3 as the assessment factor set $P$, where $P=\left(P_A,P_B,P_C,P_D\right)$, and the sub-criteria layer indicators as the second-level factor set $V_i=\left(V_1,V_2,V_3,\dots ,V_n\right)\left(i=1,2,3,\dots ,n\right)$.

(2)

Use the Likert five-point scale as the assessment grade standard, and set the evaluation set R = (R₁, R₂, R₃, R₄, R₅) = (Very Satisfied, Satisfied, Average, Dissatisfied, Very Dissatisfied). Give distinct assessment scores to relevant assessment levels after value assignment: $r=\left(90,80,70,60,50\right)$.

(3)

Assessment professionals were asked to evaluate the performance of every scheme indicator and acquired the fuzzy comprehensive assessment matrix $E$ for each indication:

$$E_A=\left[\begin{array}{ccccc}3& 2& 1& 0& 0\\ 3& 1& 1& 0& 1\\ 4& 1& 0& 1& 0\\ 3& 1& 1& 1& 0\end{array}\right]$$

(11)

$$E_B=\left[\begin{array}{ccccc}4& 1& 0& 1& 0\\ 5& 1& 0& 0& 0\\ 3& 2& 1& 0& 0\\ 2& 2& 1& 1& 0\\ 2& 3& 0& 0& 1\end{array}\right]$$

(12)

$$E_C=\left[\begin{array}{ccccc}3& 2& 1& 0& 0\\ 3& 1& 0& 2& 0\\ 2& 1& 0& 3& 0\\ 4& 1& 0& 1& 0\\ 2& 1& 2& 1& 0\end{array}\right]$$

(13)

$$E_D=\left[\begin{array}{ccccc}5& 0& 1& 0& 0\\ 4& 2& 0& 0& 0\\ 3& 1& 2& 0& 0\end{array}\right]$$

(14)

The matrices $E_A$, $E_B$, $E_C$, and $E_D$ are obtained by normalizing the matrices, respectively:

$$E_A=\left[\begin{array}{ccccc}0.5000& 0.3333& 0.1667& 0& 0\\ 0.5000& 0.1667& 0.1667& 0& 0.1667\\ 0.6667& 0.1667& 0& 0.1667& 0\\ 0.5000& 0.1667& 0.1667& 0.1667& 0\end{array}\right]$$

(15)

$$E_B=\left[\begin{array}{ccccc}0.6667& 0.1667& 0& 0.1667& 0\\ 0.8333& 0.1667& 0& 0& 0\\ 0.5000& 0.3333& 0.1667& 0& 0\\ 0.3333& 0.3333& 0.1667& 0.1667& 0\\ 0.3333& 0.5000& 0& 0& 0.1667\end{array}\right]$$

(16)

$$E_C=\left[\begin{array}{ccccc}0.5000& 0.3333& 0.1667& 0& 0\\ 0.5000& 0.1667& 0& 0.3333& 0\\ 0.3333& 0.1667& 0& 0.5000& 0\\ 0.6667& 0.1667& 0& 0.1667& 0\\ 0.3333& 0.1667& 0.3333& 0.1667& 0\end{array}\right]$$

(17)

$$E_D=\left[\begin{array}{ccccc}0.8333& 0& 0.1667& 0& 0\\ 0.6667& 0.3333& 0& 0& 0\\ 0.5000& 0.1667& 0.3333& 0& 0\end{array}\right]$$

(18)

Utilizing the weighted average fuzzy operator, via the synthesis operation of the index weights in Table 4, Table 5, Table 6 and Table 7 and the corresponding evaluation matrix $E$, the evaluation weight vector $Z$ of the criterion layer index of the design scheme can be determined as follows:

$$Z_A=W_A\times E_A=\left(\begin{array}{ccccc}0.5355& 0.2319& 0.1313& 0.0734& 0.0281\end{array}\right)$$

(19)

$$Z_B=W_B\times E_B=\left(\begin{array}{ccccc}0.5158& 0.2910& 0.0641& 0.0990& 0.0301\end{array}\right)$$

(20)

$$Z_C=W_C\times E_C=\left(\begin{array}{ccccc}0.4417& 0.2217& 0.1283& 0.2084& 0\end{array}\right)$$

(21)

$$Z_D=W_D\times E_D=\left(\begin{array}{ccccc}0.6660& 0.1457& 0.1883& 0& 0\end{array}\right)$$

(22)

Consequently, it is possible to create a fuzzy comprehensive evaluation matrix of target layer indicators:

$$Z_P=\left[\begin{array}{c}{Z}_A\\ Z_B\\ Z_C\\ Z_D\end{array}\right]=\left[\begin{array}{ccccc}0.5355& 0.2319& 0.1313& 0.0734& 0.0281\\ 0.5158& 0.2910& 0.0641& 0.0990& 0.0301\\ 0.4417& 0.2217& 0.1283& 0.2084& 0\\ 0.6660& 0.1457& 0.2319& 0& 0\end{array}\right]$$

(23)

Compute the comprehensive evaluation vector for the parent–child interactive exercise bike design scheme:

$$Q=W_P\times Z_P=\left(\begin{array}{ccccc}0.5323& 0.2347& 0.1189& 0.0955& 0.0189\end{array}\right)$$

(24)

Finally, the total assessment score for the scheme is calculated:

$$L=Q\times r=81.6726$$

(25)

Expert scholars were invited to evaluate the design scheme of a home entrance disinfectant device using the fuzzy comprehensive evaluation method [32]. The final score of the scheme was 83.45, falling within the range of 80–90, indicating that the scheme has relatively good feasibility [32]. In this study, based on the corresponding relationship between the assessment levels R = (R₁, R₂, R₃, R₄, R₅) = (Very Satisfied, Satisfied, Average, Dissatisfied, Very Dissatisfied) and assessment scores $r=\left(90,80,70,60,50\right)$, the ultimate score of the design scheme is 81.6726, i.e., falling between very satisfied and satisfied, suggesting that it is feasible and also reflects strong alignment with user needs and the design scheme.

4.7. User Satisfaction Survey

To further verify the feasibility of the design scheme, we invited 12 parents in Wuhan to evaluate it, following the same assessment process as that of the expert panel.

(1)

Parents were asked to evaluate the performance of every scheme indicator and acquired the fuzzy comprehensive assessment matrix $E$ for each indication:

$$E_A=\left[\begin{array}{ccccc}7& 3& 1& 1& 0\\ 6& 3& 1& 1& 1\\ 8& 4& 0& 0& 0\\ 7& 2& 2& 1& 0\end{array}\right]$$

(26)

$$E_B=\left[\begin{array}{ccccc}6& 4& 0& 1& 1\\ 7& 3& 0& 2& 0\\ 6& 5& 1& 0& 0\\ 5& 4& 2& 0& 1\\ 8& 2& 2& 0& 0\end{array}\right]$$

(27)

$$E_C=\left[\begin{array}{ccccc}5& 5& 0& 2& 0\\ 8& 3& 0& 1& 0\\ 6& 4& 1& 0& 1\\ 9& 2& 1& 0& 0\\ 5& 3& 3& 0& 1\end{array}\right]$$

(28)

$$E_D=\left[\begin{array}{ccccc}7& 4& 1& 0& 0\\ 6& 4& 0& 1& 1\\ 6& 3& 1& 1& 1\end{array}\right]$$

(29)

(2)

The matrices $E_A$, $E_B$, $E_C$, and $E_D$ are obtained by normalizing the matrices, respectively, and then utilizing the weighted average fuzzy operator via the synthesis operation of the index weights in Table 4, Table 5, Table 6 and Table 7 and the corresponding evaluation matrix $E$, the evaluation weight vector $Z$ of the criterion layer index of the design scheme can be determined. Finally, it is thus possible to create a fuzzy comprehensive evaluation matrix of target layer indicators:

$$Z_P=\left[\begin{array}{c}{Z}_A\\ Z_B\\ Z_C\\ Z_D\end{array}\right]=\left[\begin{array}{ccccc}0.5871& 0.2487& 0.0847& 0.0656& 0.0140\\ 0.5169& 0.3012& 0.0858& 0.0466& 0.0495\\ 0.4981& 0.3158& 0.0848& 0.0627& 0.0387\\ 0.5311& 0.3018& 0.0627& 0.0521& 0.0521\end{array}\right]$$

(30)

(3)

Likewise, it is possible to compute the comprehensive evaluation vector for the parent–child interactive exercise bike design scheme:

$$Q=W_P\times Z_P=\left(\begin{array}{ccccc}0.5394& 0.2863& 0.0816& 0.0570& 0.0358\end{array}\right)$$

(31)

(4)

Ultimately, the total assessment score for the scheme is calculated:

$$L=Q\times r=82.3742$$

(32)

Based on the corresponding relationship between the assessment levels and assessment scores, the ultimate score of the design scheme falls between very satisfied and satisfied. It indicates that parents are relatively satisfied with the design scheme.

In addition, we invited 10 parent–child families to conduct satisfaction interviews regarding the design of the parent–child interactive exercise bike, targeting both parents and children separately. By conducting separate interviews with parents and children, we can more accurately obtain the needs and feedback of different user groups. The Informed Consent form can be found in the Supplementary Materials. The User Satisfaction Interview Outline can be found in

Table A1. Parent version: user satisfaction interview outline for parent–child interactive exercise bike design.

Module	Content
User Background Information	1. What is your family structure? (e.g., children’s ages, number of children, etc.)
	2. What are your and your child’s exercise habits? Do you have a fixed workout schedule?
	3. Have you used similar fitness equipment before? If yes, please describe your experience.
Overall Satisfaction	1. How satisfied are you with the overall design of the parent–child interactive exercise bike?
	2. What part of the design appeals to you the most?
	3. What aspects of the design need improvement?
Flow Experience	1. Ease of Operation: Are you satisfied with the device’s operation process design? (e.g., startup, settings, mode switching)
	2. Real-Time Feedback: Are you satisfied with the real-time feedback design (e.g., speed, distance, calories burned)?
	3. Interface Design: Are you satisfied with the device’s interface design? (e.g., simplicity, suitability for children)
	4. Task Coherence: Are you satisfied with the design guidance for transitions between tasks (e.g., smooth switching, seamless connections)?
Clutch Experience	1. Task Completion and Reward Mechanism: Are you satisfied with the feedback design after task completion (e.g., celebration animations, sound effects, rewards)?
	2. Emotional Design: During the final stretch or when encountering difficulties, are you satisfied with the device’s interaction and emotional connection design?
Parent–Child Interaction	1. Interaction Formats: Are you satisfied with the interaction formats provided by the design (e.g., competitions, cooperative tasks, virtual adventures)?
	2. Fun and Engagement: Do you think the device’s fun design (e.g., gamified tasks, virtual scenarios) can attract you and your child?
	3. Parent–Child Emotional Experience: Do you think this design can effectively enhance parent–child relationships or foster a sense of competition and cooperation?
Safety and Space Optimization	1. Safety: Are you satisfied with the device’s safety design (e.g., antislip, speed limits, emergency braking)?
	2. Space Optimization: Are you satisfied with the device’s size and storage features (e.g., foldable design, lightweight and portable)?
Other Suggestions	1. What suggestions do you have for improving the overall design?
	2. What additional features or characteristics do you think the device should have?

and

Table A2. Child version: user satisfaction interview outline for parent–child interactive exercise bike design.

Module	Content
Overall Satisfaction	1. Do you like using this exercise bike with your parents? Why or why not?
Flow Experience	1. Ease of Operation: Are you satisfied with the device’s operation process design? (e.g., startup, settings, mode switching)
Clutch Experience	1. Task Completion and Reward Mechanism: Are you satisfied with the feedback design after task completion (e.g., celebration animations, sound effects, rewards)?
Interestingness	1. Which features of the design do you find the most attractive? (e.g., races, adventure tasks)
	2. Do you think the interface design of the exercise bike and the app is nice? What parts do you like?
Interaction Experience	1. What tasks do you enjoy completing with your parents? (e.g., competitions, cooperative tasks)
	2. Do you think this design makes playing together more fun? Why or why not?
Safety and Comfort	1. Are you satisfied with the safety design of the device? (e.g., anti-slip, speed limits, emergency braking)
	2. Do you like the appearance design of the exercise bike? Do you think it looks cool?
Other Suggestions	1. What else would you like the exercise bike to do? (e.g., tell stories, play music)

. The parent version of the outline focuses on functionality, safety, and flow–clutch experience, while the child version places greater emphasis on interestingness and interactivity. This role-based interview approach can provide a more comprehensive basis for optimizing the design scheme. Before interviewing the children, we invited the parents to sign an Informed Consent form. The Informed Consent form can be found in the Supplementary Materials. The interviews with 10 parent–child families revealed high satisfaction with the design, particularly its ability to promote family bonding, engagement, and safety. Parents appreciated features like the foldable design and safety mechanisms, while children enjoyed the interactive games and real-time feedback. However, both groups suggested improvements, such as enhancing durability, adding more games, and incorporating advanced features like AI-based parent–child interaction. These insights will guide future design iterations to better meet user needs and expectations.

5. Discussion

The existing literature on fitness bikes has largely focused on single-person usage and has separated fitness equipment designed for parents and children, with few studies combining fitness equipment aimed at both parents and children; furthermore, most existing fitness facilities are based on flow states, with little research conducted on flow and clutch. The use of flow and clutch states, AHP, QFD, and FBS methodologies, and theories in the design of parent–child interactive exercise bikes is examined in this research, offering an organized and scientific method for creative parent–child interactive exercise bike design. First, we investigated user demands according to flow and clutch states, and then we built a hierarchical model of user demand indicators for parent–child interactive fitness bikes focused on four factors: structure and styling, interactive experience, performance requirements, and emotional belonging. This is a useful reference for future designs and evaluations of parent–child interactive exercise bikes. Furthermore, utilizing the AHP enabled the exact quantification and prioritizing of user demands for parent–child fitness bikes, resulting in an improved understanding of user demands and final product position. Subsequently, the implementation of QFD made it possible to categorize and decompose technical characteristics in an orderly way. This facilitated the creation of parent–child interactive exercise bikes in a thorough and well-organized manner, offering significant backing for the later achievement of a device incorporating the desired technical details and the conversion of technical features into functions. Finally, the structural rationality and functional accomplishment of the device were guaranteed through the methodical mapping of functional demands into particular product behavior factors and structural modules via the successive deployment of the FBS model. The efficacy of clutch and flow and the use of AHP-QFD-FBS as the basis for guidance was confirmed by the thorough assessment findings of the creative design layout for parent–child interactive exercise bikes.

Through the sequential application of the AHP, QFD, and FBS methodologies to the complementing strengths of these three theoretical approaches, this study successfully offers more precise and comprehensive theoretical guidelines for the complete conceptual design procedure of interactive exercise bikes for parents and children. By enhancing the creativity, viability, and user approval of design plans, this study helps upcoming designers create more methodical and professional parent–child interactive exercise bike designs. This research advances the study of designing fitness facilities for children and promotes social and familial well-being. Furthermore, this study provides an established technique and strategy for conceptualizing the layout of various kinds of products in addition to enhancing the academic and operational investigations in parent–child interactive exercise bike design.

The integration of the flow–clutch state theory with AHP-QFD-FBS represents a significant advancement in the field of product design. By combining psychological theories with systematic design methodologies, this approach enables designers to create products that are not only functionally robust but also emotionally engaging. The flow–clutch state theory provides insights into users’ psychological needs, while AHP-QFD-FBS offers a structured framework for translating these needs into functional and structural design solutions. Traditional design methods often focus solely on functional requirements, whereas our approach also considers users’ psychological states, leading to more engaging products. For example, existing parent–child exercise bikes may lack the features that our design incorporates, such as interactive gaming modules and real-time feedback mechanisms.

Beyond parent–child exercise bikes, integrating the flow–clutch state theory with AHP-QFD-FBS promises extensive practical applications. For example, it can be applied to design fitness equipment for elderly users that promotes flow states through engaging activities and supports clutch states through attention and effort guidance, thereby enhancing physical activity and well-being among older adults. Similarly, the approach can be adapted for corporate wellness programs, where interactive fitness equipment designed for office environments could provide immersion experience guidance to promote employee health and productivity. Additionally, the methodology can be extended to the design of educational toys that balance learning and play, keeping children engaged while adapting to their learning needs and skill levels. Finally, the approach is highly suitable for healthcare and rehabilitation equipment, where user engagement and adaptability are critical for effective treatment and recovery. By applying this design framework across these diverse contexts, products can achieve a balance between functionality and user experience, ultimately enhancing their effectiveness and user satisfaction.

However, this study still has certain limitations. On the one hand, a small expert panel may not fully capture the diverse perspectives needed to comprehensively evaluate the design. For example, experts from different cultural or professional backgrounds might provide additional insights that could improve the design. A small sample size reduces the statistical power of the analysis, making it more difficult to draw definitive conclusions, whereas a larger panel could provide more robust and reliable results. On the other hand, the user testing involved a relatively small and homogeneous group, which may not represent the broader population. For example, families from different cultural, socioeconomic, or geographic backgrounds might have different needs and preferences that were not fully captured in this study. A larger and more diverse user group could provide a more balanced and representative evaluation of the design.

6. Conclusions

In conclusion, this study has societal relevance in addition to both significant theoretical and actual application value. Apart from parent–child exercise bikes, this design approach holds potential for broader practical implementations. For example, it can be used to develop fitness equipment for elderly users and healthcare rehabilitation devices. In each context, integrating the flow–clutch state theory with systematic design methodologies can help create products that balance user skills and capacity, enhancing both functionality and user experience. Future research should expand the sample size of experts and users and ensure that the evaluators have diverse cultural and social backgrounds as much as possible to provide more objective design evaluation results.