Emotion Recognition from Human Gait Features Based on DCT Transform

Xue, Penghui; Li, Baobin; Wang, Ning; Zhu, Tingshao

doi:10.1007/978-3-030-37429-7_51

Cited by 4 publications

(2 citation statements)

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“…Scholars in science education have researched noncognitive abilities in CT from various perspectives and levels. For example, Xue et al [12] studied computer vision, finding that students had an advantage in attention retention but lacked sustained and stable executive control. Additionally, Tang et al [13] systematically investigated non-cognitive abilities in CT from theoretical and experimental perspectives.…”

Section: Introductionmentioning

confidence: 99%

Research and Development of Computational Thinking Assessment for College Students Integrating Cognitive and Non-Cognitive Abilities

Zhang,

Li,

Zeng

et al. 2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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Currently, the tools for assessing the computational thinking(CT) abilities of college students lack comprehensive coverage, focusing only on the cognitive aspects of CT and neglecting exploration of non-cognitive abilities. In this study, through a literature review, a theoretical framework for college students' CT was constructed, encompassing both cognitive and non-cognitive abilities. The framework includes four core dimensions: conceptual knowledge, algorithmic thinking, problem exploration, and values, along with 11 secondary indicators. Based on this framework, a test tool suitable for assessing college students' CT was developed. The tool consists of 18 questions assessing non-cognitive abilities and 22 questions assessing cognitive abilities. Statistical analysis methods and multidimensional item response theory were employed to analyze the empirical results of the test. The results indicate that the tool has reasonable difficulty and discrimination; the α reliability coefficient method shows good internal consistency; factor analysis results demonstrate a good fit between the theoretical model and observed data, indicating high structural validity. Overall, the test tool exhibits high quality and performance, and the comprehensive dimensionality provides important guidance for the reform and development of CT in higher education.

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Section: Introductionmentioning

confidence: 99%

Research and Development of Computational Thinking Assessment for College Students Integrating Cognitive and Non-Cognitive Abilities

Zhang,

Li,

Zeng

et al. 2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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“…Human emotions can be perceived not only through explicit facial expressions [1], voice information [2], or text cues [3], but also through implicit body language, including eye movements [4], body postures [5], and gait traits [6]. Nonverbal communication plays a major role in recent human-robot interaction (HRI) [7].…”

Section: Introductionmentioning

confidence: 99%

Data augmentation by separating identity and emotion representations for emotional gait recognition

Sheng

2023

Robotica

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Human-centered intelligent human–robot interaction can transcend the traditional keyboard and mouse and have the capacity to understand human communicative intentions by actively mining implicit human clues (e.g., identity information and emotional information) to meet individuals’ needs. Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.

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