Machine Learning and Sensor Data Fusion for Emotion Recognition

Abstract

In this article, the authors investigate the development of sensor data fusion-based emotion detection models. They use direct and continuous sensor data to construct emotion prediction models. They use sensor data fusion involving the environmental and physiological signals. This article integrates on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: 1) collecting in the wild data set of multiple sensors; 2) using data fusion, feature fusion, and decision fusion for emotion recognition; 3) prediction of emotional states based on fusing environmental and physiological variables; 4) developing subject-dependent emotion detection models. To achieve that, they have done a real-world study “in the wild” with physiological and mobile sensors. The datasets come from participants walking around Minia University campus. The authors compared the obtained results to choose the best-performing model. Results show that D Tand RF outperforms SVM and KNN significantly by 1% or 2% (p <0.01) with an average accuracy of 0.97%.