Cloud-Edge Collaborative Depression Detection Using Negative Emotion Recognition and Cross-Scale Facial Feature Analysis

Depression is a mental disorder that causes pain to people and society and is also the largest cause of disability in the world. Intelligent early screening of depression is of great benefit for patients to obtain better diagnoses and treatment. However, previous low-precision detection methods based on facial vision heavily rely on computing resources, which hinders the wide application of automatic depression diagnoses. Therefore, this article proposes an intelligent method for multiscene automatic depression symptom detection, which uses an efficient and convenient cloud-edge collaboration framework combined with negative emotion monitoring and cross-scale facial feature analysis. We deploy a shallow model (EdgeER) on the edge server and a deep model (C-DepressNet) on the cloud server. EdgeER is used to quickly detect negative user emotions and screen user data. C-DepressNet is used to analyze degrees of depression with high precision. The experimental results show that our cloud-edge collaboration framework has superior performance in depression detection accuracy and service response times.