In order to provide a set of quantitative evaluation of the comfort of hand-held sports products, this study preliminarily constructed the comfort evaluation index system of hand-held sports products from the perspectives of performance, tactile characteristics, quality and appearance, physical stimulation, discomfort, morphological characteristics, user demographic characteristics and hand characteristics. Secondly, 165 groups of data were collected through the experiment, and factor analysis was used to reduce the dimension of the original index. Then neural network was used to establish the mapping relationship between the evaluation index and the comfort evaluation, and the evaluation model of the comfort of hand-held sports products was obtained. The results show that the relative error between the predicted value and the actual value of the network model is basically less than 5% in the practical application of the evaluation method taking the badminton racket as an example, which reflects that the comfort evaluation method can effectively reduce the correlation between the original indexes, achieve the accurate evaluation of the comfort of products, and verify the effectiveness and feasibility of the evaluation method. The combination of neural network and factor analysis can quantitatively analyze and evaluate the comfort of hand-held sports products and provide reliable suggestions for manufacturers to design and produce more comfortable products in the form of data.
Aims The software interface design is changing and updating and has spawned a large number of interface icon design requirements. Using deep learning and generative adversarial network(GAN) to assist icon design can simplify the process of icon design and enrich the creativity of icon conception. Methods The Python tool was used to capture nearly 2000 icon data and perform a simple filtering. After the steps of icon preprocessing, data transformation, structure design of generating network and discriminant network, definition of loss function, model recording and saving, a generation adversarial network suitable for icon design is successfully constructed. Results In this study, icon generation is well explored by combining deep learning and generative adversarial networks. Although the change of function loss and the output icon quality are not ideal in the preliminary practice, higher quality and new icons are obtained by improving the icon recognition algorithm, and the function loss value is reduced. The research has certain guiding significance for more personalized, higher quality and efficient icon generation and design in the future. Conclusion It is feasible to apply deep learning and GAN into icon design. The newly generated icons have certain artistic expression and instructional intention, and can be used as ideas and creativity in icon design process.
Aiming at the emerging issues of Chinese aging population in the context of social digitization, the elderly behaviours and habits when using applications on smart phone were studied in order to understand the inconvenience and obstacle they often encounter. Through mining and analyzing scatter data obtained by capturing the gaze areas and touch locations on the screen, the method of nested square data statistics was used in different modules of user interface. Data fit and variance analysis were conducted by utilizing MatLab and probabilistic models of Mathematical Statistics, which could set up the behaviours mathematical model of elderly user when using mobile applications. Finally, a principle of "senior mode" user interface design for mobile applications was proposes.
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