| dc.description.abstract | Psychological quality plays a crucial role in mental health and is typically assessed through the use of questionnaires and scales, which can be expensive and time-consuming. However, recent research has shown promising alternatives for assessing psychological quality. These include analyzing various sources such as text, audio, facial attributes, heart rate, and eye movement. In this paper, we propose the development of a virtual therapist specifically designed for automatic psychological quality assessment on mobile devices. This virtual therapist would actively engage users in voice dialogue, adapting the conversation content based on emotion perception. Throughout the conversation, we extract features from multiple modalities including text, audio, facial attributes, heart rate, and eye movement, enabling a comprehensive assessment of psychological quality. We utilize two fusion frameworks for automatic psychological quality analysis and machine learning to classify the varying sets of dimensions and factors, which include depression, body, excitement, instability, anxiety, family care, independence, melancholic, manic, and anxiety as well as the family relationship. Based on the data collected from 168 participants, the experimental results demonstrate the effectiveness of our fusion framework utilizing five modal features. The highest accuracy rates were achieved for various psychological factors including depression, body, excitement, instability, anxiety, family care, independence, melancholic tendencies, manic tendencies, anxiety tendencies, and family relationship. Specifically, the fusion framework achieved accuracy rates of 68.66 percent, 74.66 percent, 72.06 percent, 93.65 percent, 70.66 percent, iii 72.66 percent, 93.33 percent, 68.66 percent, 84.43 percent, 70.66 percent, and 72 percent, respectively, for these factors. These findings highlight the robustness and reliability of our approach in accurately assessing and predicting various aspects of psychological well-being. | en_US |