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Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data
16 references·0 citing·43 second hopsampled
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data (2020)Can We Ditch Feature Engineering? End-to-…Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition (2020)Exploring temporal representa…Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits (2020)Datasets for Cognitive Load I…Wearable-Based Affect Recognition—A Review (2019)Wearable-Based Affect Recogni…Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework (2018)Long Short Term Memory Hyperp…Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection (2018)Introducing WESAD, a Multimod…Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection (2018)Deep learning analysis of mob…Monitoring stress with a wrist device using context (2017)Monitoring stress with a wris…Recognition of emotions using multimodal physiological signals and an ensemble deep learning model (2016)Recognition of emotions using…ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors (2016)ASCERTAIN: Emotion and Person…A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns (2016)A novel feature extraction me…DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses (2015)DECAF: MEG-Based Multimodal D…cStress (2015)cStressPublication and other reporting biases in cognitive sciences: detection, prevalence, and prevention (2014)Publication and other reporti…Learning deep physiological models of affect (2013)Learning deep physiological m…DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2011)DEAP: A Database for Emotion …Detecting Stress During Real-World Driving Tasks Using Physiological Sensors (2005)Detecting Stress During Real-…A circumplex model of affect. 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References

  • 2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals5,082 cited
  • 2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors2,205 cited
  • 2018Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection1,356 cited
  • 2016ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors538 cited
  • 2014Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention471 cited
  • 2016Recognition of emotions using multimodal physiological signals and an ensemble deep learning model416 cited
  • 2015DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses367 cited
  • 2017Monitoring stress with a wrist device using context353 cited
  • 2015cStress340 cited
  • 2018Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection293 cited
  • 2013Learning deep physiological models of affect287 cited
  • 2019Wearable-Based Affect Recognition—A Review219 cited
  • 2020Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition211 cited
  • 2018Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework135 cited

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  • 2020Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits101 cited
  • 2016A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns98 cited
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