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Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring
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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.
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Cited by 2 Pith papers
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Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions
Narrative review of cognitive-impairment detection technologies concludes that reported accuracies are often inflated by weak validation and that progress depends on multimodal, longitudinally validated, externally te...
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Seamless Integration: The Evolution, Design, and Future Impact of Wearable Technology
A whitepaper-style survey arguing that user-centered design, ethical practice, and sustainability will determine the future success of wearable technology.
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