Liu et al., 2020 - Google Patents
Emotion Recognition Through Observer's Physiological SignalsLiu et al., 2020
View PDF- Document ID
- 13499403924471788193
- Author
- Liu Y
- Gedeon T
- Caldwell S
- Lin S
- Jin Z
- Publication year
- Publication venue
- arXiv preprint arXiv:2002.08034
External Links
Snippet
Emotion recognition based on physiological signals is a hot topic and has a wide range of applications, like safe driving, health care and creating a secure society. This paper introduces a physiological dataset PAFEW, which is obtained using movie clips from the …
- 230000037007 arousal 0 abstract description 27
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0476—Electroencephalography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/164—Lie detection
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0531—Measuring skin impedance
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tarnowski et al. | Eye‐Tracking Analysis for Emotion Recognition | |
Udovičić et al. | Wearable emotion recognition system based on GSR and PPG signals | |
Zhu et al. | Detecting emotional reactions to videos of depression | |
Zhou et al. | Tackling mental health by integrating unobtrusive multimodal sensing | |
Bhatt et al. | Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions | |
Bara et al. | A Deep Learning Approach Towards Multimodal Stress Detection. | |
Zhou et al. | Confusion state induction and EEG-based detection in learning | |
Zhang et al. | Multi-modal interactive fusion method for detecting teenagers’ psychological stress | |
Kadar et al. | Affective computing to enhance emotional sustainability of students in dropout prevention | |
Rad et al. | Stereotypical motor movement detection in dynamic feature space | |
Cross et al. | Comparing, differentiating, and applying affective facial coding techniques for the assessment of positive emotion | |
Ferrari et al. | Using voice and biofeedback to predict user engagement during requirements interviews | |
Ivonin et al. | Beyond cognition and affect: sensing the unconscious | |
Liu et al. | Emotion Recognition Through Observer's Physiological Signals | |
Masmoudi et al. | Meltdowncrisis: Dataset of autistic children during meltdown crisis | |
Gaudi et al. | Affective computing: an introduction to the detection, measurement, and current applications | |
Guhan et al. | Developing an effective and automated patient engagement estimator for telehealth: A machine learning approach | |
Yang et al. | Wearable Structured Mental-Sensing-Graph Measurement | |
Katsumata | A multiple smart device-based personalized learning environment | |
D’Mello | Automated mental state detection for mental health care | |
Regin et al. | Use of a Fatigue Framework to Adopt a New Normalization Strategy for Deep Learning-Based Augmentation | |
Zhu et al. | Electrodermal activity for emotion recognition using cnn and bi-gru model | |
Yang et al. | A Multimodal Dataset for Mixed Emotion Recognition | |
Hsiao et al. | Emotion inference of game users with heart rate wristbands and artificial neural networks | |
Bakkialakshmi et al. | Effective Prediction System for Affective Computing on Emotional Psychology with Artificial Neural Network |