面向动态场景的智能产品交互认知负荷评估方法研究

In order to reduce the problems of inefficient human-computer interaction and poor user experience caused by cognitive load overload in dynamic scenarios, a user cognitive load experience matching model for multi-task interaction of intelligent products is constructed to evaluate the relationship between cognitive load, interaction tasks and user experience in dynamic scenarios. Considering the dynamic impact of scene changes on user needs, the model first combines questionnaire analysis to understand the tasks that users interact with high frequency, high attention span and long time, and combines the out-of-sound thinking method to sort out the user experience map in different scenarios to realize the visual mapping of tasks to experience and pain points in each scenario, and then analyses the cognitive load quantification index of each interaction task in different scenarios through Subjective Workload Assessment Technique (SWAT) The user experience is then matched with the cognitive load indicators to filter out the tasks and scenarios where the user experience is poor due to cognitive load overload. The cognitive load theory is used to optimize the interaction design and propose solutions for dynamic scenarios. The fuzzy KANO model is used to analyze the satisfaction of functional requirements in dynamic scenarios and to determine the hierarchy of functional requirements priority. Using a smart cycling product as an object, the functional shape and interaction interface of the product are designed and tested to verify the usability and validity of the above method and provide a reference for the development of intelligent product interaction design in dynamic scenarios.