With the current global outbreak of COVID-19, an increasing number of people are suffering from negative mental states and mental disorders. We propose a multimodal psychological computational technology in a universal environment. We establish a mental health database following a naturalistic paradigm as well as a long-term ubiquitous interpretable psychological computing model based on prior knowledge and multimodal information fusion. The proposed model achieves state-of-the-art accuracy in both basic and complex emotion detection on the proposed mental health database and effectively solves scientific and accuracy-related problems in long-term complex mental health status recognition and prediction. Regarding psychology and the medicine of mental disorders, we identify the continuous emotional symptoms of three kinds of mental disorders, which have not previously been accurately observed based on multimodal big data. They are accurately and quantitatively described by the newly introduced interpretable psychological computing model. At the same time, we establish the relationship between two complex emotions and the basic emotions, breaking through the cognitive limitations of the traditional psychology field.

中文翻译：

---

借助深层视觉分析实现感官情感：一种长期的心理建模方法

随着当前全球COVID-19的爆发，越来越多的人患有负面的精神状态和精神障碍。我们提出了一种在通用环境中的多模式心理计算技术。我们遵循自然主义范式以及基于先验知识和多模式信息融合的长期普遍存在的可解释性心理计算模型来建立心理健康数据库。所提出的模型在所提出的心理健康数据库上实现了基本和复杂情感检测的最新准确性，并有效解决了长期复杂心理健康状态识别和预测中与科学和准确性相关的问题。关于心理和精神障碍的医学，我们确定了三种精神障碍的持续情感症状，基于多模式大数据以前没有被准确地观察到。通过新引入的可解释的心理计算模型可以准确，定量地描述它们。同时，我们突破了传统心理学领域的认知局限，建立了两种复杂情感与基本情感之间的关系。