sotirismos/emotion-recognition-conversations
Diploma thesis analyzing emotion recognition in conversations exploiting physiological signals (ECG, HRV, GSR, TEMP) and an Attention-based LSTM network
This project helps researchers and developers build systems that can automatically detect human emotions during conversations. By analyzing continuous physiological signals (like heart rate, skin conductivity, and temperature) from wearable devices, it classifies emotions into levels of arousal and valence in real time. It's designed for those working on emotionally intelligent machines or human-computer interaction.
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Use this if you are developing or researching real-time emotion recognition systems primarily using physiological data from wearable sensors.
Not ideal if your primary data sources are facial expressions, speech, or brain signals (EEG), or if you need to recognize a wide range of discrete emotions beyond arousal and valence.
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28
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4
Language
Python
License
MIT
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Last pushed
Sep 11, 2022
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