Faculty of Computers and Information Minia University, Minia 61519, Egypt.
Faculty of Computers and Information Minia University, Al-Obour High Institute for Management, Computers and Information systems, Obour, Cairo 999060, Egypt.
Sensors (Basel). 2022 Jul 27;22(15):5611. doi: 10.3390/s22155611.
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.
人类情感的自动识别不是一个简单的过程。有许多内部和外部因素会影响情感。情感表达也可以通过多种方式进行，例如文本、语音、身体姿势，甚至通过生理身体反应。情感检测可以实现许多应用，例如自适应用户界面、互动游戏、人机交互等。移动设备、传感器和数据分析工具等先进技术的可用性使得能够从各种来源收集数据，这使研究人员能够准确预测人类情感。目前大多数研究都在实验室实验中使用这些技术来收集数据。在这项工作中，我们使用直接和实时传感器数据来构建一个与主体无关（通用）的多模态情感预测模型。这项研究集成了身体上的生理标记、周围的感觉数据和情感测量，以实现以下目标：（1）收集包括环境、身体反应和情感的多模态数据集。（2）基于融合环境和生理变量，创建独立于主体的情感状态预测模型。（3）评估集成学习方法，并比较它们的性能，以创建具有高精度的通用独立主体情感识别模型，并将结果与之前的类似研究进行比较。为了实现这一目标，我们使用生理和移动传感器进行了现实世界的“野外”研究。数据集的收集来自于参与者在米尼亚大学校园周围散步的数据，以创建准确的预测模型。使用了各种集成学习模型（Bagging、Boosting 和 Stacking），将以下基础算法（K 近邻 KNN、决策树 DT、随机森林 RF 和支持向量机 SVM）结合作为基础学习者，将 DT 作为元分类器。结果表明，与其他集成学习方法的变体相比，集成堆叠学习技术的准确率最高，达到 98.2%。相反，Bagging 和 Boosting 方法的准确率分别为 96.4%和 96.6%。
