A hybrid approach to emotion recognition system using multi-discriminant analysis &amp; k-nearest neighbour

Jain, Monika; Saini, Sandeep; Kant, Vibhor

doi:10.1109/icacci.2017.8126181

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…A total of 145 features were extracted from ECG and respiration by Cheng he et al [7]. Feature extraction methods encompass Empirical Mode Decomposition (EMD) [42][43][44] and Hilbert Huang Transform (HHT) [3,9,15], wavelet [45,46],Fourier transform [47,48]. Jerrita et al [12] employed an algorithm to integrate Empirical mode decomposition along with Hilbert transform and Fourier analysis extracted entropy and power of raw ECG signal.…”

Section: Feature Extractionmentioning

confidence: 99%

An Emotion Recognition Based on Physiological Signals

2019

IJITEE

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Emotion recognition is alluring considerable interest among the researchers. Emotions are discovered by facial, speech, gesture, posture and physiological signals. Physiological signals are a plausible mechanism to recognize emotion using human-computer interaction. The objective of this paper is to put forth the recognition of emotions using physiological signals. Various emotion elicitation protocols, feature extraction techniques, classification methods that aim at recognizing emotions from physiological signals are discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other physiological signal for emotion detection. Working on basic as well as non-basic human emotion and human-computer interface will make the system robust.

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Section: Feature Extractionmentioning

confidence: 99%

An Emotion Recognition Based on Physiological Signals

2019

IJITEE

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Stock Market Predictions Using FastRNN, CNN, and Bi-LSTM-Based Hybrid Model

Yadav

Saini

2021

Lecture Notes in Electrical Engineering

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Exploring Wrist Pulse Signals using Empirical Mode Decomposition: Emotions

Garg

Arvind

Kaur

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Emotion recognition is attracting considerable interest among the research community. In this work, Empirical Mode Decomposition has been implemented to derive both statistical and nonlinear features from Wrist Pulse Signal to classifying emotions namely anxiety and boredom. Wrist Pulse signals were extracted from 24 subjects using TETRIS game as a stimulus using Fission and Fusion approach. The acquired signals were pre-processed to remove unwanted noise and artefacts present within the signal. In addition, various classifiers namely Naiive Byes, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis were considered. Results from these classifiers indicate that both Logistic Regression and Quadratic Discriminant Analysis gave an indistinguishable accuracy of 99.71% (fission) and 77.08% (fusion) for anxiety state. Moreover, for boredom state, the highest classification accuracy was 66.67 % for Naiive Bayes using fission and 64.58% for fusion. Results highlight the impact of empirical mode decomposition with hilbert transform for the recognition of emotion from wrist pulse signals.

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A hybrid approach to emotion recognition system using multi-discriminant analysis & k-nearest neighbour

Cited by 5 publications

References 12 publications

An Emotion Recognition Based on Physiological Signals

An Emotion Recognition Based on Physiological Signals

Stock Market Predictions Using FastRNN, CNN, and Bi-LSTM-Based Hybrid Model

Exploring Wrist Pulse Signals using Empirical Mode Decomposition: Emotions

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