Emotion recognition via random forest and galvanic skin response: Comparison of time based feature sets, window sizes and wavelet approaches

Ayata, Değer; Yaslan, Yusuf; Kamasak, Mustafa E.

doi:10.1109/tiptekno.2016.7863130

Cited by 49 publications

(32 citation statements)

References 4 publications

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“…The valence-arousal dimensional model, represented in Fig. 1, is widely used for emotion representation [13,14,18,20].…”

Section: Emotion Representationmentioning

confidence: 99%

“…In our first study on this topic, we investigated emotion recognition only from GSR signals [13,14]. Later, we used PPG and GSR signals together and proposed a data fusion based emotion recognition method for music recommendation engines [15].…”

Section: Introductionmentioning

confidence: 99%

“…extracted features and their corresponding formulas[14]. From thisTable,arithmetic mean value, maximum value, minimum value, standard deviation, variance, skewness, kurtosis, median, zero crossings, entropy, mean energy, moments, and change in signal values have been considered as features.…”

mentioning

confidence: 99%

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Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

2020

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The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems. In this work, physiological signals are collected from a respiratory belt (RB), photoplethysmography (PPG), and fingertip temperature (FTT) sensors. These signals are used as their collection becomes easy with the advance in ergonomic wearable technologies. Methods Arousal and valence levels are recognized from the fused physiological signals using the relationship between physiological signals and emotions. This recognition is performed using various machine learning methods such as random forest, support vector machine and logistic regression. The performance of these methods is studied. Results Using decision level fusion, the accuracy improved from 69.86 to 73.08% for arousal, and from 69.53 to 72.18% for valence. Results indicate that using multiple sources of physiological signals and their fusion increases the accuracy rate of emotion recognition. Conclusion This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature. It is shown that decision level fusion from multiple classifiers (one per signal source) improved the accuracy rate of emotion recognition both for arousal and valence dimensions.

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“…The valence-arousal dimensional model, represented in Fig. 1, is widely used for emotion representation [13,14,18,20].…”

Section: Emotion Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

2020

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“…Datasets such as DEAP [13], LIRIS-ACCEDE [14], RECOLA [15] and MAHNOB-HCI [16] dermal activity as an important indicator of the attentional and emotional state of a person. Nevertheless, most authors have typically focused in the prediction of the EEG signal, while the modelling of EDA responses using content-based audiovisual features has been tackled less often [17].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Predicting Group-Level Skin Attention to Short Movies from Audio-Based LSTM-Mixture of Experts Models

Kleinlein¹,

Jiménez²,

Montero³

et al. 2019

Interspeech 2019

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Electrodermal activity (EDA) is a psychophysiological indicator that can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli like audiovisual content. EDA measurements are not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception, and group-level EDA recordings integrate the reaction of an audience, thus reducing the signal noise. This paper contributes to the field of audience's attention prediction to video content, extending previous novel work on the use of EDA as ground truth for prediction algorithms. Videos are segmented into shorter clips attending to the audience's increasing or decreasing attention, and we process videos' audio waveform to extract meaningful aural embeddings from a VG-Gish model pretrained on the Audioset database. While previous similar work on attention level prediction using only audio accomplished 69.83% accuracy, we propose a Mixture of Experts approach to train a binary classifier that outperforms the main existing state-of-the-art approaches predicting increasing and decreasing attention levels with 81.76% accuracy. These results confirm the usefulness of providing acoustic features with a semantic significance, and the convenience of considering experts over partitions of the dataset in order to predict group-level attention from audio.

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“…Graphical data analysis of the STFT showed notable increase in the power spectrum across a range of frequencies directly following fault events. GSR was used in [ 14 ] for emotion recognition by extracting time domain and wavelet based features. Features were extracted using various window lengths.…”

Section: Introductionmentioning

confidence: 99%

Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses

Dehzangi

Rajendra

Taherisadr

2018

Sensors

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One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been conducted to automatically detect driver distraction. Although camera-based techniques have been successfully employed to characterize driver distraction, the risk of privacy violation is high. On the other hand, physiological signals have shown to be a privacy preserving and reliable indicator of driver state, while the acquisition technology might be intrusive to drivers in practical implementation. In this study, we investigate a continuous measure of phasic Galvanic Skin Responses (GSR) using a wristband wearable to identify distraction of drivers during a driving experiment on-the-road. We first decompose the raw GSR signal into its phasic and tonic components using Continuous Decomposition Analysis (CDA), and then the continuous phasic component containing relevant characteristics of the skin conductance signals is investigated for further analysis. We generated a high resolution spectro-temporal transformation of the GSR signals for non-distracted and distracted (calling and texting) scenarios to visualize the associated behavior of the decomposed phasic GSR signal in correlation with distracted scenarios. According to the spectrogram observations, we extract relevant spectral and temporal features to capture the patterns associated with the distracted scenarios at the physiological level. We then performed feature selection using support vector machine recursive feature elimination (SVM-RFE) in order to: (1) generate a rank of the distinguishing features among the subject population, and (2) create a reduced feature subset toward more efficient distraction identification on the edge at the generalization phase. We employed support vector machine (SVM) to generate the 10-fold cross validation (10-CV) identification performance measures. Our experimental results demonstrated cross-validation accuracy of 94.81% using all the features and the accuracy of 93.01% using reduced feature space. The SVM-RFE selected set of features generated a marginal decrease in accuracy while reducing the redundancy in the input feature space toward shorter response time necessary for early notification of distracted state of the driver.

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Emotion recognition via random forest and galvanic skin response: Comparison of time based feature sets, window sizes and wavelet approaches

Cited by 49 publications

References 4 publications

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Predicting Group-Level Skin Attention to Short Movies from Audio-Based LSTM-Mixture of Experts Models

Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses

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