Human emotion recognition for enhanced performance evaluation in e-learning

Abstract

In interpersonal relationships, the recognition of human emotion plays an important role. Speech, hand and gestures of the body and facial expressions reflect emotions. Therefore, the interaction between human and machine communication plays a high role in extracting and understanding emotion. The challenging characteristic in e-learning includes lack of learners' motivation, the belief that e-learning offers no support, and the learners' busy schedules are considered essential. In this paper, heuristic multimodal real-time emotion recognition approach (HMR-TER) has been proposed to provide timely and appropriate online feedback based on learners' vocal intonations and facial expressions to foster their learning. Hybrid validation dynamic analysis is introduced that e-learning professionals must overcome an overall lack of learner motivation. e-Learning requires greater consciousness, encouragement and autonomy than academic learning. The Hybrid validation dynamic analysis provides a template for students to become and continue to stay motivated with attention, trust and satisfaction. Adequate gesture recognition Analysis is presented to take an e-learning course because they think that they would not be able to go at their own pace or require a great deal of time. E-learning achievement focuses significantly on the development and amount of asynchronous online communications. To decrease the feeling of alienation in E-learning, it should be adequately supervised. This exclusion is one of the significant causes of e-learning efficiency and common blockages. The gesture recognition analysis to be carried out in this field seeks to bring consolidation remedies to grasp and recognize manual gestures from an intimate picture. The simulation analysis proves how to enhance the quality and efficiency of e-learning by including the learner's emotional states. Emotion significantly impacts human brain functions, such as perceived notion, alertness, acquiring knowledge, cognition, thinking and problem-solving. A feeling has a heavy impact on enhancing the learning process, significantly modifying attention specificity and encouraging activity and behaviors. The final results are obtained as the face detection ratio to 84.25%, the hand gestures ratio to 92.70%, the voice recognition ratio to 82.26%, the reduction of the emotion problems ratio to 84.5% and the efficiency of the e-learning ratio to 93.85%.