5G technology brings a comprehensive improvement in the network layer, which meets real-time, high-efficiency, and stability requirements in medical scenarios to a large extent, such as remote diagnosis and surgery. The heavy burden and severe impact of mental disorders make it desirable to find quantitative and automatic assessment approaches for early-stage detection of mental disorders. Facial expressions contain abundant emotional information, which may reflect abnormal mental states like anxiety and depression. With low latency and high bandwidth, 5G makes real-time monitoring of mental health feasible. In this article, a novel undisturbed mental state assessment prototype is proposed, which uses facial video streaming collected with 5G terminals to assess the mental state of a user in real time. A case study of depression detection using facial expressions has been developed based on the prototype. As a study case, we collected facial expression data from patients with depression and healthy people as control subjects. We extracted the transitional optical flow under stimulus feature and used the decision tree for classification. Results show that our depression assessment model is effective, and also reflect the feasibility and validity of our prototype.

中文翻译：

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5G时代无干扰心理状态评估：基于面部表情的抑郁症检测案例研究


5G技术带来网络层的全面提升，很大程度上满足远程诊断、手术等医疗场景的实时、高效、稳定的需求。精神障碍的沉重负担和严重影响使得人们需要寻找定量和自动评估方法来早期检测精神障碍。面部表情蕴含着丰富的情绪信息，可能反映焦虑、抑郁等异常心理状态。 5G具有低延迟和高带宽的特点，使心理健康的实时监测成为可能。本文提出了一种新颖的无干扰心理状态评估原型，利用5G终端采集的面部视频流来实时评估用户的心理状态。基于原型开发了使用面部表情检测抑郁症的案例研究。作为一个研究案例，我们收集了抑郁症患者和健康人作为对照对象的面部表情数据。我们提取了刺激特征下的过渡光流并使用决策树进行分类。结果表明我们的抑郁症评估模型是有效的，也体现了我们原型的可行性和有效性。