Design of walking aids for the elderly based on the Kano-AHP-FEC method (2025)

Keywords: Product innovation design, User journey mapping, Kano, AHP, FCE, Population aging

Subject terms: Health care, Engineering

User needs acquisition

To gain a deeper understanding of the latent needs of elderly users, this study conducted behavioral observations of 16 individuals over the age of 60 who require walking aids. The analysis focused on identifying problems encountered and potential needs during their outings, as illustrated in Fig.2.

By examining the behavioral journey diagram of elderly users, we can identify pain points and opportunities in walker design. During the use process, we can determine the functional needs (Z1) of the elderly by analyzing these pain points. Additionally, their psychological needs (Z2) can be identified through the observation of 16 elderly individuals. A detailed analysis of the appearance requirements (Z3) is provided in Table 1.

Design of Kano questionnaire for elderly walking aids

In this study, the KANO model is applied to classify and assess various user needs related to walker design, clarifying how specific functions and features impact user satisfaction. The KANO model categorizes user needs into five types: Must-be Requirements (M), One-dimensional Requirements (O), Attractive Requirements (A), Indifferent Requirements (I), and Reverse Requirements (R)⁴⁸. This process establishes a clear prioritization of design features, providing a solid foundation for subsequent AHP-based design analysis. Detailed user requirements for elderly walker design are presented in Table 2.

When designing the Kano questionnaire, responses are recorded from both positive and negative perspectives. Satisfied needs are addressed with positive questions, while unmet needs are addressed with negative questions. Each question has five response options: Like, Must-be, Neutral, Live with, and Dislike, corresponding to scores of 5, 4, 3, 2, and 1, respectively. Based on the results of the Kano questionnaire, the demand attributes of each element are determined according to the evaluation criteria in Table 3.

In a provincial capital city in central China, two nursing homes with a capacity of more than 100 people were selected for questionnaire analysis. Through the questionnaire survey, the design elements with high priority in the design requirements of walkers were obtained. The preliminary questionnaire design is shown in Table 4. The complete questionnaire can be found in Appendix S1. A total of 100 questionnaires were distributed and 93 valid questionnaires were collected. 63% of the questionnaires were returned by males and 37% by females, with an age range of 60–75years old.

Reliability Analysis: After collecting data from the questionnaire, a reliability analysis is necessary to ensure the data’s dependability for further calculations. As shown in Table 5, the Cronbach’s α values for both the positive and negative items in the questionnaire are greater than 0.8. This indicates that the data from the questionnaire are highly reliable and suitable for subsequent analysis.

Validity Analysis: Validity analysis is performed to determine whether the questionnaire design and the data collected are reasonable. Using SPSS software for data analysis, the KMO value was found to be 0.764, and the Bartlett’s test of sphericity yielded an approximate chi-square value of 540.43, as shown in Table 6. These results indicate high validity of the questionnaire. Therefore, detailed user data obtained can be used to calculate user satisfaction based on the functional requirements of the walking aid.

Satisfaction Index, SI) and Dissatisfaction Index, DSI), where SI is usually positive and DSI is usually negative. See formulas (1) and (2).

Better-Worse coefficient indicates the selection ratio of each demand, where SI tends to 1, then satisfying the demand can improve the user’s satisfaction, and DSI tends to −1, then not satisfying the demand can improve the satisfaction. See Table 7 for the analysis results of Kano questionnaire user needs.

Take the better value as the Y-axis coordinate and the worse value as the X-axis coordinate, and establish the image limit diagram. It can be seen from Fig.3 that the expected demand of A2, A9, A5 and A12 should be met first, and the expected demand of A11 and A7 should be met preferentially.

User needs analysis based on hierarchical analysis

Based on the Kano questionnaire results, Basic Needs (M), Performance Needs (O), and Excitement Needs (A) significantly impact user satisfaction. In contrast, Indifferent Needs (I) have a minimal effect on user satisfaction according to the walker requirements analysis. Consequently, 10 out of 12 design elements were selected as evaluation criteria. Next, a hierarchical structure model was developed using the Analytic Hierarchy Process (AHP). The model includes three layers: the Goal Layer, the Criteria Layer, and the Indicator Layer. Details are presented in Table 8.

To determine the priority ranking of elements in the indicator layer, this study enlisted five experts in the field of medical rehabilitation for scoring and evaluation. Their profiles are as follows: Three Senior Physical Therapists: Each therapist has over five years of experience in elderly rehabilitation and physical recovery. Their extensive industry knowledge provides a deep understanding of the needs and preferences of elderly users, which helps accurately identify user requirements for walker products. One Chief Rehabilitation Physician: This physician has significant experience in elderly rehabilitation and chronic disease management. They have led multiple rehabilitation research projects and published numerous academic papers. Their expertise offers valuable medical insights into the physiological changes and rehabilitation needs of the elderly. One Rehabilitation Psychotherapy Counselor: With over five years of experience, this counselor specializes in elderly mental health and emotional support. Their comprehensive understanding of elderly psychological needs, emotional support, and rehabilitation therapy provides critical information for addressing the psychological and emotional aspects of walker design. The scoring criteria are detailed in Table 9. The weights of the criterion-level attributes are presented in Table 10, and the weights for secondary needs are shown in Tables 11, 12 and 13.

To verify the consistency of the weights for the criterion level and sub-criterion level, we conducted consistency checks using Formula (3). The results of the consistency checks are shown in Table 14. All consistency ratios (CR) are below 0.1, indicating that the consistency requirements have been met.

Combine the weight of criterion layer with the weight of sub-criterion layer, and the final result of comprehensive weight calculation is as follows in Table 15.

The use of the hierarchical model and judgment matrix ensures the scientific and rational selection of design elements. Designers can prioritize these elements based on their importance as shown in Table 15 when developing walkers for the elderly.

Design practice

It can be seen from the above that according to the extracted design elements as the key design elements of the walker design, the preliminary design of the scheme is carried out, and the sketch is shown in Fig.4.

The designer outputs the scheme according to the creative sketch and produces the experimental prototype, as shown in Fig.5.

Scheme 1: This design features a robust all-metal frame to ensure stability on various surfaces. The handles are made of wood to enhance grip comfort and include a non-slip feature. The bottom of the walker is equipped with a storage compartment for convenient storage of personal items.

Scheme 2: This design utilizes lightweight aluminum alloy for easy portability. The minimalist Scandinavian style adds to its visual appeal. To accommodate different terrains, the walker has wide wheels. The rear wheels are designed to rotate 360 degrees, facilitating maneuverability in tight spaces.

Scheme 3: This design incorporates a modular approach, allowing the walker to be assembled and adjusted according to user needs. The handles, seat, and support legs are detachable for easy cleaning and replacement. The extended front-to-back axle provides enhanced stability. Additionally, the design includes an adjustable seat for resting and a braking system on all wheels for added safety. This proposal is ideal for users who require personalized adjustments and frequent outings.

Design scheme evaluation

The Fuzzy Comprehensive Evaluation (FCE) method is highly suitable for quantifying and analyzing abstract and ambiguous data. In this study, the primary reason for using the Fuzzy Comprehensive Evaluation (FCE) method is to scientifically select the optimal design solution, minimizing the influence of individual subjective factors. To ensure the scientific nature of the research, we assembled a review panel of 10 experts and users. The panel consists of 2 full-time senior caregivers with over 10years of experience in elderly care, 3 elderly individuals with over 5years of experience using walking aids, 2 engineers specializing in medical assistive devices, and 2 rehabilitation therapists with more than 5years of clinical experience.

Based on the weight vectors obtained from the indicator layer, a fuzzy comprehensive evaluation matrix was constructed. The review panel was invited to score and rank the three proposed solutions. By comparing these scores, the optimal solution was selected. For instance, to illustrate the process using Scheme 1:

1. According to the factors selected by the experts, establish the factor set and establish the evaluation factor index set U, U = {$U_M,U_O,U_A$}.

2. Establish the comment set and evaluation criteria: V = {$V_1,V_2,V_3,V_4$}, representing {very satisfied, satisfied, average, dissatisfied} respectively, and the corresponding scoring format is V = (90, 75, 60, 50).

3. Establish the fuzzy matrix R. Use the percentage method to calculate the membership degree and determine the fuzzy matrix R, as shown in formula (4).

Taking Scheme 1 as an example, the necessary attribute evaluation matrix in the sub-criteria layer is represented by $R_M$, the desired attribute is represented by $R_O$, and the attractive attribute is represented by $R_A$. The results are as follows:

$$R_M=\left[\begin{array}{cccc}0.7& 0.2& 0.1& 0.0\\ 0.6& 0.2& 0.2& 0.0\\ 0.7& 0.3& 0.0& 0.0\end{array}\right]$$

$$R_O=\left[\begin{array}{cccc}0.7& 0.2& 0.1& 0.0\\ 0.6& 0.2& 0.2& 0.0\\ 0.7& 0.3& 0.0& 0.0\\ 0.7& 0.2& 0.1& 0.0\end{array}\right]$$

$$R_A=\left[\begin{array}{cccc}0.8& 0.2& 0.0& 0.0\\ 0.6& 0.2& 0.2& 0.0\\ 0.6& 0.3& 0.1& 0.0\end{array}\right]$$

4. Determine the weight vector of the evaluation factor, W = {$a_1,a_2,...,a_p$}. According to Tables 10, 11 and 12 above, the weight vector of the criterion layer is $W_U=${$0.54545,0.18182,0.27273$}, and the weight vectors of the sub-criterion layer are $W_M=${$0.17169,0.38688,0.44143$};

$W_O=${$0.11181,0.11181,0.41597,0.36042$}; $W_A=${$0.50077,0.29563,0.20361$}.

5. According to the determined weight vector W and matrix R, calculate the fuzzy evaluation vector X, as shown in formula (5).

Calculate the evaluation weight vector X of the criterion layer indicators of Scheme 1:

$$X_M=W_M\circ R_M=\left(0.6610,.,2440.0950,.,000\right)$$

$$X_O=W_O\circ R_O=\left(0.6890,.,2310.0800,.,000\right)$$

$$X_A=W_A\circ R_A=\left(0.7000,.,2210.0790,.,000\right)$$

The final comprehensive evaluation vector for Scheme 1 is:

$$P=W_U\circ X_U=W_U\circ \left[\begin{array}{c}{X}_M\\ X_O\\ X_A\end{array}\right]=\left(0.6770,.,2350.0880,.,000\right)$$

6. Calculate the comprehensive percentage score of Scheme 1:

Through the weighted calculation of the comprehensive evaluation vector P and the score corresponding to the comment set levels, the percentage score of Scheme 1 is 83.835. According to the above method, the score of solution 2 is 85.515, and the score of solution 3 is 87.045. The specific calculation process and data of Scheme 2 and Scheme 3 can be found in Appendix S2. From the scores, we can see that Scheme 3 is the best option.

Feasibility evaluation and verification

To ensure the accuracy and reasonableness of the evaluation results and the feasibility of the evaluation methods, researchers often employ standardized questionnaires to assess user experience. These questionnaires help validate the evaluation results and determine whether the product effectively meets the needs of elderly users based on their feedback. The commonly used standardized questionnaires include the Questionnaire for User Interaction Satisfaction (QUIS)⁴⁹, the Post-Study System Usability Questionnaire (PSSUQ)⁵⁰, the System Usability Scale (SUS)⁵¹, and the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)⁵². The QUEST questionnaire, in particular, is designed to provide a systematic way for users of assistive technology to evaluate their satisfaction⁵³.

Figure5 displays the functional prototypes of the three proposed solutions. To validate these prototypes, 100 elderly individuals with experience using relevant products from a nursing home in Zhengzhou City were selected for the study. The participants consisted of 63 males and 37 females, with ages distributed as follows: 33 people aged 60–65, 45 people aged 66–70, and 22 people over 70years old. The duration of use of the walking aid products among participants was categorized as 23 individuals using them for 1–3years, 38 for 3–5years, and 39 for over 5years. All participants were volunteers, and the study was classified as a social investigation. No human research was conducted, and data collection was anonymous, ensuring compliance with ethical standards.

Each of the 100 elderly users tested the three product prototypes and completed the QUEST questionnaire based on their experience. As shown in Table 16. The questionnaire used a 9-point Likert scale, with 1 indicating strong disagreement and 9 indicating strong agreement. According to the QUEST standards, the products were evaluated across six indexes: comfort, safety, durability, ease of use, practicality, and appearance design. Each index was derived from the average value of its corresponding items. Higher scores reflect greater satisfaction, while lower scores indicate less satisfaction. A total of 100 questionnaires were distributed, and 92 valid responses were obtained after screening. The αcoefficient of the questionnaire calculated using SPSS software was 0.982, indicating that the reliability was high and met the requirements.

The six indexes of comfort, safety, durability, ease of use, practicality and appearance design of the three schemes are compared and analyzed. Figure6 shows that the score of Scheme 3 is significantly higher than that of the other two schemes. The user evaluation results are consistent with the comprehensive assessment obtained through the Fuzzy Comprehensive Evaluation (FCE) method used in this study, indicating that FCE is both applicable and feasible for evaluating and selecting design options for elderly walkers.

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Supplementary Materials

Data Availability Statement

All relevant data that supports the findings of this study are available within the manuscript.