## 3 Methods
### Research Tools  
#### 3.3.1 The Fuzzy Delphi Expert Questionnaire  
Since most of the multi-criteria decision-making factors need to draft partial criteria for strategy and evaluation via literature and interviews, they may not be completely applicable. Accordingly, this study conducted a fuzzy Delphi questionnaire after constructing a questionnaire based on expert validity. The questionnaire was formed with a 10-point scale, and fuzzy Delphi experts were asked to evaluate the importance of each indicator by filling in \"the best single value (optimal value)\", \"the lowest acceptable value (minimum value)\", and \"the highest acceptable value (maximum value)\", respectively, and an open opinion column was also offered, aiming to establish double triangle fuzzy numbers and gray zone tests, as well as calculate consensus values. In particular, the \"grey zone tests\" can effectively examine whether experts show a consistent convergence effect in cognition, so that fuzzy Delphi experts can refer to them to see if they need to revise their opinions until all evaluation items can reach a convergence and obtain the \"consensus importance value\". This study conducted two fuzzy Delphi questionnaires in total. After repeated revisions, mergers, additions and subtractions of the questionnaires, the \"key factor indicators for the wearable device product design\" were finally summarized, organized, and selected. 3.2 The Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process Questionnaire  
The Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process (DNAP) method applied by this study is the multi-criteria decision-making (MCDM) model generated by the combination of the Decision-Making Trial and Evaluation Laboratory method and the Analytic Network Process method, as explained below. The exploration of the cause-and-effect relationship and correlation between product design indicators for wearable devices was performed using the Decision-Making Trial and Evaluation Laboratory method. First, the DANP expert questionnaire was designed with reference to the evaluation scale proposed by [PERSON] and [PERSON] (2008). The evaluation scale was set as \"(0) no impact\", \"(1) low impact\", \"(2) moderate impact\", \"(3) high impact\", \"(4) extremely high impact\" [72]. Then, the influence values of factors judged by experts were collected to establish the average expert opinion matrix, the normalized average expert opinion matrix, and the total impact relationship matrix. Subsequently, the threshold value was set to eliminate the criteria or indicators which had an overly low impact in the total impact relationship matrix, thereby obtaining a simplified total impact relationship matrix, based on which the correlation in the cause-and-effect diagram was plotted. In addition, with the help of the cause-and-effect diagram, decision-makers can also plan appropriate product designstrategies based on the cause factors in the criteria or indicators and their interplay relationships. The exploration of the weight value and ranking of product design indicators for wearable devices using the Analytic Network Process method was performed based on the Decision-Making Trial and Evaluation Laboratory method. In this study, the weighting and ranking of product design indicators for wearable devices were determined by integrating the Analytic Network Process (ANP) proposed by [PERSON] (1996) with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The process began with the calculation of the average expert _matrix_ (A) and the normalized average expert opinion _matrix_ (D) using DEMATEL. After performing overall normalization, _the total influence relationship matrix_ (\(T_{c}\)) was derived. Subsequently, the normalized benchmark (\(f_{i}\)) of the total influence relationship matrix was established to obtain the _normalized total influence relationship matrix_ (\(T_{c}^{*}\)), which was transposed to form the _unweighted supermatrix_ (W).