Wearable Emotion Recognition System based on GSR and PPG Signals

Udovičić, Goran; Ðerek, Jurica; Russo, Mladen; Sikora, Marjan

doi:10.1145/3132635.3132641

Cited by 88 publications

(46 citation statements)

References 14 publications

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“…Most often, skin conductions is used as the main parameter in this technique. Electrical parameters of the skin are not under conscious human control [78] since, according to the traditional theory, they depend on the variation of sweat reaction, which reflects changes in the sympathetic nervous system [79]. There is proof that some output signals from sympathetic nervous bursts are followed by the changes of skin conductance [80].…”

Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%

“…Compared to ECG and EEG, GSR gives less information about emotional state, but it has a few important advantages: (i) it requires fewer measuring electrodes, which allows for the easier use of wearable devices and definition of emotional states when a person engages in normal activities; (ii) GSR provides fewer raw data, especially if long term monitoring is performed, this allows to analyse obtained data more quickly and does not require a lot of computational power; (iii) equipment required for GSR measurements is much more simple and cheaper, if special electrodes are available, a measuring device can be assembled using popular and freely available components (ADC converters, microcontrollers, etc.). Since a GSR signal contains useful information related with its amplitude and frequency, usually, it is analyzed in time and frequency domains by applying various techniques and extracting such statistical parameters as: median, mean, standard deviation, minimum, maximum, as well as ratio of minimum and maximum [78]. The application of traditional signal analysis methods for GSR measurements is complicated by the fact that a signal contains low and high frequency components, and a reaction to the same stimulus is not always identical.…”

Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%

See 1 more Smart Citation

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%

Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

View full text Add to dashboard Cite

show abstract

“…Most previous work [14,23,31] solve this problem by manually designing handcrafted features across different sensors and fusing them at decision level [29], which is timeconsuming and often leads to low accuracies. Other works [1,28,33] segment or pad the signals to let them have fixed lengths and train the data with neural networks. These kinds of methods could lead to over-fitting problems because of mislabeled segmentations and limited amount of data.…”

Section: Introductionmentioning

confidence: 99%

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

Zhang¹,

Ali

Wang³

et al. 2019

2019 International Conference on Multimodal Interaction

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To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods.

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“…Many physiological modalities and features have been evaluated for ER, namely Electroencephalography (EEG) [ 28 , 29 , 30 ], Electrocardiography (ECG) [ 31 , 32 , 33 ], Electrodermal Activity (EDA) [ 34 , 35 , 36 ], Respiration (RESP) [ 26 ], Blood Volume Pulse (BVP) [ 26 , 35 ] and Temperature (TEMP) [ 26 ]. Multi-modal approaches have prevailed; however, there is still no clear evidence of which feature combinations and physiological signals are the most relevant.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

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Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

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Wearable Emotion Recognition System based on GSR and PPG Signals

Cited by 88 publications

References 14 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

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