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

2015 ◽  
pp. 301-306 ◽  
Author(s):  
Chaka Koné ◽  
Imen Meftah Tayari ◽  
Nhan Le-Thanh ◽  
Cecile Belleudy
Keyword(s):  
Physiological Signals ◽  
Healthcare Applications ◽  
Decision Level ◽  
Recognition Of Emotions ◽  
Decision Level Fusion ◽  
Level Fusion

scholarly journals Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

2020 ◽  
Vol 40 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Değer Ayata ◽  
Yusuf Yaslan ◽  
Mustafa E. Kamasak
Keyword(s):  
Emotion Recognition ◽  
Healthcare Systems ◽  
Physiological Signals ◽  
Support Vector ◽  
Multiple Sources ◽  
Accuracy Rate ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Arousal And Valence

Abstract Purpose 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.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

Object Detection Based On Decision Level Fusion

2019 ◽  
Author(s):  
Jun Yao ◽  
Yong Zhang ◽  
Fang Liu ◽  
Yi-cong Liu
Keyword(s):  
Object Detection ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

Decision Level Fusion Framework for Face Authentication System

2011 ◽  
pp. 413-427 ◽  
Author(s):  
V. Vaidehi ◽  
Teena Mary Treesa ◽  
N. T. Naresh Babu ◽  
A. Annis Fathima ◽  
S. Vasuhi ◽  
...  
Keyword(s):  
Decision Level ◽  
Face Authentication ◽  
Authentication System ◽  
Decision Level Fusion ◽  
Fusion Framework ◽  
Level Fusion
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