Study on the Differences of Icon Cognition of Graphical Interface for Age-Friendly Design.

Icon cognitive impairment is one of the main difficulties for older users to use smart products. This study explores the impact of icon style, icon concreteness and both together on understanding icons by comparing the cognitive differences between two groups of users, young and old. The performance of different types of icons on cognitive performance was analyzed specifically through a comprehensive cognitive load assessment method of behavioral, physiological and subjective data. The results showed that the cognitive performance of skeuomorphism concrete icons was higher and the cognitive load was lower when the user group was novice or older users with less experience in interacting with the GUI; the cognitive performance of flat concrete icons was higher and the cognitive load was lower when the user group was familiar or young users with more experience in interacting with the GUI. From the perspective of cognitive differences between young and old, the results of this study provide a reference for future icon design of interactive interfaces for older people smart terminals to help designers and developers achieve a better user experience for the aging group, which has practical significance for the age-friendly design of graphical interfaces.

sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization.

The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals. The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few. However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures. Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures. The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture. The support vector machine (SVM) is used to assist verification. Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model. Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared. The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures. The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user's interactive experience.