This research project aims to tackle the growing mental health challenges in today's digital age. It employs a modified pre-trained BERT model to detect depressive text within social media and users' web browsing data, achieving an impressive 93% test accuracy. Simultaneously, the project aims to incorporate physiological signals from wearable devices, such as smartwatches and EEG sensors, to provide long-term tracking and prognosis of mood disorders and emotional states. This comprehensive approach holds promise for enhancing early detection of depression and advancing overall mental health outcomes.

翻译：本研究旨在应对当今数字时代日益严峻的心理健康挑战。采用改进的预训练BERT模型，对社交媒体及用户网页浏览数据中的抑郁倾向文本进行检测，测试准确率达93%。同时，该项目拟整合可穿戴设备（如智能手表与脑电传感器）的生理信号，实现情绪障碍与情感状态的长期追踪与预后评估。这种综合性方法有望提升抑郁症的早期检出率，并推动整体心理健康水平的改善。