BackgroundWith the development trend of healthy aging and intelligent integration, escort products have become a new means of healthy aging. Healthy old-age care pays attention to the convenience and informatization of life. To meet the needs, designers often design multiple accompanying product solutions, and it is very important to use reasonable evaluation methods to decide on the optimal solution.PurposesA new comprehensive evaluation method is proposed to reduce the subjectivity and one-sidedness of the selection process of intelligent escort product design solutions, and to make the decision more objective and reasonable. Such decisions can enhance the experience and naturalness of the elderly using intelligent products.MethodsFirst, a large number of user interviews were analyzed using the grounded theory, gradually refine through theoretical coding, and abstracted with the design scheme evaluation index. Second, the idea of game-theoretic weighting is used to optimize a linear combination of subjective and objective weights to determine the final weights of each evaluation indicator. Finally, the evaluation and selection are completed based on the solution ranking determined by the approximate ideal solution ranking method (TOPSIS). It is applied for the selection of the elderly escort robot design, and the usability test is conducted using the PSSUQ to verify the selection results.ResultsA new comprehensive evaluation method can better complete the preferential selection of product design solutions for healthy aging escorts, and reduce the subjectivity and one-sidedness of the evaluation.ConclusionThis method compensates for the reliance on personal experience in the selection of options, and improve the subjectivity of the evaluation index determination process and the deviation of index weighting. Improving the objectivity and scientificity of decision-making reduces the blindness of design and production. It also provides a theoretical reference for the research scholars of healthy aging companion products.

Long series time forecasting has become a popular research direction in recent years, due to the ability to predict weather changes, traffic conditions and so on. This paper provides a comprehensive discussion of long series time forecasting techniques and their applications, using the Informer algorithm model as a framework. Specifically, we examine sequential time prediction models published in the last two years, including the tightly coupled convolutional transformer (TCCT) algorithm, Autoformer algorithm, FEDformer algorithm, Pyraformer algorithm, and Triformer algorithm. Researchers have made significant improvements to the attention mechanism and Informer algorithm model architecture in these different neural network models, resulting in recent approaches such as wavelet enhancement structure, auto-correlation mechanism, and depth decomposition architecture. In addition to the above, attention algorithms and many models show potential and possibility in mechanical vibration prediction. In recent state-of-the-art studies, researchers have used the Informer algorithm model as an experimental control, and it can be seen that the algorithm model itself has research value. The informer algorithm model performs relatively well on various data sets and has become a more typical algorithm model for time series forecasting, and its model value is worthy of in-depth exploration and research. This paper discusses the structures and innovations of five representative models, including Informer, and reviews the performance of different neural network structures. The advantages and disadvantages of each model are discussed and compared, and finally, the future research direction of long series time forecasting is discussed.

After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction.

In the context of the rapid development of navigation technology and the deepening of users' diversified needs, as an emerging public service, mobile AR (Augmented Reality) navigation is supposed to focus on human-computer interaction and user experience. To extract the influencing factors of efficient service of mobile AR navigation, we constructed an experimental method for the usability of mobile AR navigation and the users' emotional experience based on behavior-emotion analysis. In this study, the user types were divided according to the differences in Mental Cutting Ability and Gender. We explored the effects of Interaction Mode, Mental Cutting Ability, and Gender on the usability of mobile AR navigation and the users' PAD (Please-Arousal-Dominance) three-dimensional emotion through the objective performance and subjective scoring of users when completing AR navigation tasks. The results showed that the Interaction Mode and Mental Cutting Ability had significant effects on the usability of mobile AR navigation and the users' emotional experience; the Ease of Learning, Ease of Use in usability indicators, and the Arousal experience of three-dimensional emotion were significantly affected by Gender. Based on the experimental results, we excavated the mechanism of effects between various factors, extracted the behavioral and emotional trends of different types of users, broadened the research scope of mobile AR navigation-related fields, and finally summarized the design strategies from the perspective of human-robot-environment.