Multimodal Emotion Recognition by Fusing Video Semantic Information in MOOC Learning Scenarios

Khái niệm cốt lõi

Learners' emotions in MOOC learning scenarios are closely related to the semantic information of instructional videos, and fusing video semantic information with physiological signals can effectively improve the performance of emotion recognition.

Tóm tắt

The paper proposes a multimodal emotion recognition method that integrates video semantic information and physiological signals (eye movement and PPG) to improve emotion recognition performance in MOOC learning scenarios.

Key highlights:

• The authors hypothesize that learners' emotions in MOOC learning are closely related to the semantic information of instructional videos. To explore this, they propose a multimodal emotion recognition method that fuses video semantic information and physiological signals.
• The video semantic information is obtained by generating video descriptions using a pre-trained large language model (LLM). This high-level semantic information is then fused with eye movement and PPG signals using a cross-attention mechanism.
• The fused multimodal features are fed into an emotion classifier for final emotion prediction. Experiments show that incorporating video semantic information significantly improves emotion recognition performance, with an accuracy improvement of over 14%.
• The authors also adopt adaptive synthetic sampling (ADASYN) to address the issue of imbalanced data distribution, which further enhances the model's performance.
• Extensive experiments on both the VLMED dataset and the public MAHNOB-HCI dataset demonstrate the effectiveness and generalization ability of the proposed method.

Thống kê

The dataset used in this study contains subjects' eye movement, PPG, facial expression, and EDA data, as well as the instructional videos watched by the subjects.

Trích dẫn

"In the Massive Open Online Courses (MOOC) learning scenario, the semantic information of instructional videos has a crucial impact on learners' emotional state."
"To deeply explore the impact of video semantic information on learners' emotions, this paper innovatively proposes a multimodal emotion recognition method by fusing video semantic information and physiological signals."
"The experimental results show that our method has significantly improved emotion recognition performance, providing a new perspective and efficient method for emotion recognition research in MOOC learning scenarios."

Thông tin chi tiết chính được chắt lọc từ

Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios

by Yuan Zhang,X... lúc arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07484.pdf

Yêu cầu sâu hơn

How can the proposed method be further extended to incorporate other modalities, such as facial expressions or speech, to enhance the emotion recognition performance in MOOC learning

To further enhance the emotion recognition performance in MOOC learning scenarios, the proposed method can be extended to incorporate additional modalities such as facial expressions or speech. By integrating facial expression analysis, the model can capture more nuanced emotional cues that may not be fully captured by physiological signals alone. Facial expression recognition algorithms can be used to extract features related to emotions like happiness, sadness, anger, and surprise, providing valuable input for the emotion recognition model. Similarly, incorporating speech analysis can add another layer of emotional context to the recognition process. Speech sentiment analysis can help identify emotional cues in the tone and content of learners' speech during the learning process. By integrating these modalities alongside video semantic information and physiological signals, the model can achieve a more comprehensive understanding of learners' emotional states, leading to improved emotion recognition performance.

What are the potential limitations of using video semantic information for emotion recognition, and how can they be addressed in future research

While using video semantic information for emotion recognition in MOOC learning scenarios offers significant benefits, there are potential limitations that need to be considered. One limitation is the potential subjectivity and variability in interpreting the semantic content of videos. Different individuals may perceive and interpret the same video content differently, leading to inconsistencies in emotion recognition. To address this limitation, future research could explore methods to standardize the interpretation of video semantic information or incorporate user feedback to refine the semantic analysis process. Additionally, the quality and accuracy of the generated video descriptions can impact the overall performance of the emotion recognition model. Ensuring the accuracy and relevance of the semantic information extracted from videos is crucial for reliable emotion recognition. Future studies could focus on improving the accuracy of video description generation algorithms or exploring alternative methods for extracting semantic information from instructional videos.

How can the insights gained from this study on the relationship between video semantic information and learners' emotions be applied to the design and development of more engaging and effective MOOC learning experiences

The insights gained from the study on the relationship between video semantic information and learners' emotions can be applied to enhance the design and development of MOOC learning experiences. By understanding how video content influences learners' emotional states, course creators and educators can tailor instructional videos to evoke specific emotions that enhance learning outcomes. For example, incorporating engaging and emotionally resonant video content can help maintain learners' interest and motivation throughout the course. Additionally, leveraging video semantic information to personalize learning experiences based on individual emotional responses can lead to more adaptive and effective learning environments. By analyzing the emotional impact of different video stimuli on learners, course designers can optimize video content to create a more immersive and impactful learning experience. This personalized approach can help increase learner engagement, retention, and overall satisfaction with the MOOC platform.