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EmoAda: New AI System Aims to Provide Emotional Support Through Chat

Mar 02, 2024 - techtimes.com
EmoAda, a new AI system developed by researchers at Hefei University of Technology and Hefei Comprehensive National Science Center, aims to provide emotional support through chat. The system uses advancements in natural language processing and large language models to engage in emotional conversations, offering low-cost psychological support. It collects real-time multimodal data (audio, video, and text) from users, extracts emotional features, and uses a multimodal large language model to analyze these features for real-time emotion recognition, psychological profiling, and guidance strategy planning.

In related news, Dartmouth College researchers have developed MoodCapture, an AI-powered phone application that detects early signs of depression through facial expressions. The app uses the smartphone's front camera to analyze images for indicators associated with depression, achieving a 75% accuracy rate in identifying symptoms in individuals diagnosed with major depressive disorder. Both EmoAda and MoodCapture represent significant advancements in the use of AI for mental health support and diagnosis.

Key takeaways:

  • A new AI system, EmoAda, developed by researchers at Hefei University of Technology and Hefei Comprehensive National Science Center, aims to offer emotional support through chat by engaging in emotional conversations.
  • EmoAda collects real-time user data, including audio, video, and text inputs, to analyze emotional features and provide personalized responses, suggesting activities to alleviate stress.
  • Initial trials of EmoAda have shown promise in providing natural and humanized psychological support, with users appreciating the anonymity it offers. Some users prefer interacting with AI as it reduces anxieties about privacy breaches and social pressure.
  • In related news, researchers at Dartmouth College have developed MoodCapture, an AI-powered phone application that detects early signs of depression through facial expressions, achieving a 75% accuracy rate in identifying symptoms in individuals diagnosed with major depressive disorder.
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