Emotion Recognition System featuring a fusion of Electrocardiogram and Photoplethysmogram Features

Shahid, Hira; Butt, Aqsa; Aziz, Sumair; Khan, Muhammad Umar; Naqvi, Syed Zohaib Hassan

doi:10.1109/icosst51357.2020.9333021

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…There are five common elicitation techniques which are audio visual, imagery, music, memory recall, and the situational procedure [ 50 ]. The less common approaches are naturalistic conversations or debates [ 51 ], driving [ 52 ], video games [ 53 ], and virtual reality [ 54 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Moreover, the HHT also preserves the characteristic of frequency changes as the lengths of original signal and IMF are the same. The application of EMD for ECG feature extraction techniques to emotion recognition systems is seen in a few papers such as [ 21 , 26 , 54 , 76 , 99 , 100 ]. In [ 54 ], 35 features were extracted from IMF1 and IMF2.…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

“…The application of EMD for ECG feature extraction techniques to emotion recognition systems is seen in a few papers such as [ 21 , 26 , 54 , 76 , 99 , 100 ]. In [ 54 ], 35 features were extracted from IMF1 and IMF2. The features consist of statistical features such as mean, max, standard deviation, variance, skewness, kurtosis, and others.…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

“…When k is bigger than that, the subjected models are considered biased. The 5-fold CV was practiced in [ 54 , 74 ], while a rare 15-fold CV was only conducted in [ 54 ]. Moreover, 10-fold CV is the most widely practiced cross-validation technique, with 12 papers in total [ 6 , 26 , 39 , 47 , 53 , 54 , 55 , 88 , 99 , 112 , 114 , 115 ].…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

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Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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