Multimodal Recognition of Emotions Using Physiological Signals with the Method of Decision-Level Fusion for Healthcare Applications

Koné, Chaka; Tayari, Imen Meftah; Le-Thanh, Nhan; Belleudy, Cécile

doi:10.1007/978-3-319-19312-0_26

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Fusion of 4 physiological signals [18] 71 Method of Chaka Koné [20] 81.69 Proposed Method (UTE-Emotica) 83.2…”

Section: Resultsmentioning

confidence: 99%

Emotion Recognition based on Multimodel: Physical - Bio Signals and Video Signal

Hương¹

2019

IJERT

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Today, detecting and classifying emotions has become an important item of research and life. Emotion detection and classification are becoming more detailed and accurate thanks to development of various fields such as electronics, sensors or computer engineering. Emotion recognition methods are studied using different data collection methods and one of the most popular and effective methods is the use of physicalbio sensors. Physicalbio sensors -based approaches can provide accurate, sustainable biological information with external influences and interferences.Especially when compared with other approaches such as image processing, video processing. In this paper, we describe a method of classifying and assessing emotions based on a combination of signals collected from physicalbio sensors, video collection and machine learning methods. Specifically, we will describe the platform of a physicalbio signal collection system, the process of collecting information and the processing information system used to identify how emotional behavior is characterized. We have also shown that a combination of physicalbio sensors acquisition systems, video collection and machine learning methods can provide identification performance with an accuracy of 83.2%.

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“…Fusion of 4 physiological signals [18] 71 Method of Chaka Koné [20] 81.69 Proposed Method (UTE-Emotica) 83.2…”

Section: Resultsmentioning

confidence: 99%

Emotion Recognition based on Multimodel: Physical - Bio Signals and Video Signal

Hương¹

2019

IJERT

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Fusion of physiological measures for multimodal biometric systems

Barra

Casanova

Fraschini

et al. 2016

Multimed Tools Appl

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Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances

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Energy Modeling and Architecture Exploration for Emotion Detection Systems

Koné¹,

Belleudy²,

Le-Thanh

2016

2016 Euromicro Conference on Digital System Design (DSD)

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International audienceEnergy consumption is one of the main constraints which are faced by designers of communicating objects. Indeed, we are seeing a real increase in the number of communicating objects ranging from customer applications (video games, health care objects) to industrial applications (M2M, automatic car parking, drones). This paper presents a new high level model of autonomy estimation for wearable communicating objects, and more particularly for emotion detection systems and a design space exploration. The innovation in this methodology is to meet the autonomy constraint of health objects while maintaining a reasonable performance which is defined by the emotion recognition rate. The influence of the architecture (RF protocol, kind and number of sensors, ...) and its configuration of the system autonomy and emotion recognition rate is studied in order to propose the most suitable system. The different results show that we can get a high recognition rate and a sufficient autonomy for users

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Multimodal Recognition of Emotions Using Physiological Signals with the Method of Decision-Level Fusion for Healthcare Applications

Cited by 3 publications

References 13 publications

Emotion Recognition based on Multimodel: Physical - Bio Signals and Video Signal

Emotion Recognition based on Multimodel: Physical - Bio Signals and Video Signal

Fusion of physiological measures for multimodal biometric systems

Energy Modeling and Architecture Exploration for Emotion Detection Systems

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