Evaluating ensemble learning methods for multi-modal emotion recognition using sensor data fusion

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Younis, EMG, Zaki, SM, Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661 and Houssein, EH, 2022. Evaluating ensemble learning methods for multi-modal emotion recognition using sensor data fusion. Sensors, 22 (15): 5611. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s22155611

Abstract

Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study “in the wild” with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Younis, E.M.G., Zaki, S.M., Kanjo, E. and Houssein, E.H.
Publisher:	MDPI AG
Date:	July 2022
Volume:	22
Number:	15
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s22155611 DOI 1579835 Other
Rights:	This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	26 Sep 2022 14:32
Last Modified:	26 Sep 2022 14:32
URI:	https://irep.ntu.ac.uk/id/eprint/47124