A Two-stage Method For Hand-Raising Gesture Recognition in Classroom

Liao, Wang; Xu, Wei; Kong, SiCong; Ahmad, Fowad; Liu, Wei

doi:10.1145/3318396.3318437

Cited by 17 publications

(10 citation statements)

References 11 publications

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“…In addition, it efficiently resolves two major challenges associated with complex scenes: low resolution and motion distortion. Liao et al [ 7 ] proposed a method that involves two stages, namely pose estimation and hand gesture recognition, for identifying hand raising gestures. The aforementioned study analyzed the features of the arms, including the shoulders, elbows, and wrists, which are the main features for identifying hand raising gestures.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, many studies have used human action recognition to recognize student behaviors. However, most of these studies have focused on one specific action of students, such as hand raising gestures [ 6 , 7 , 8 , 9 ], sleep gestures [ 10 ], and yawning behavior [ 11 , 12 , 13 , 14 ]. Therefore, an efficient system that recognizes student behaviors accurately is required.…”

Section: Introductionmentioning

confidence: 99%

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Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection

Lin

Ngo

Dow

et al. 2021

Sensors

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Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper proposes a student behavior recognition system based on skeleton pose estimation and person detection. First, consecutive frames captured with a classroom camera were used as the input images of the proposed system. Then, skeleton data were collected using the OpenPose framework. An error correction scheme was proposed based on the pose estimation and person detection techniques to decrease incorrect connections in the skeleton data. The preprocessed skeleton data were subsequently used to eliminate several joints that had a weak effect on behavior classification. Second, feature extraction was performed to generate feature vectors that represent human postures. The adopted features included normalized joint locations, joint distances, and bone angles. Finally, behavior classification was conducted to recognize student behaviors. A deep neural network was constructed to classify actions, and the proposed system was able to identify the number of students in a classroom. Moreover, a system prototype was implemented to verify the feasibility of the proposed system. The experimental results indicated that the proposed scheme outperformed the skeleton-based scheme in complex situations. The proposed system had a 15.15% higher average precision and 12.15% higher average recall than the skeleton-based scheme did.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection

Lin

Ngo

Dow

et al. 2021

Sensors

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“…An often-addressed action is hand-raising. Wang et al [27] elaborated a two-stage method consisting of body pose estimation and hand-raising detection and achieved up to 95% precision and 90% recall. Lin, Jiang and Shen [28] analysed a large-scale dataset consisting of 40,000 examples of hand-raising gestures and achieved 85% overall detection accuracy.…”

Section: Measuring Engagementmentioning

confidence: 99%

“…Their method obtained an average precision of 75%. Wang, Jiang and Shen [27] addressed yawning with a dataset of 12,000 samples and reached up to 90% detection accuracy.…”

Section: Measuring Engagementmentioning

confidence: 99%

Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement

et al. 2021

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Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note-taking or hand-raising, is challenging to recognise, and does not correlate with students’ self-reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured.

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“…Using technology in classroom has shown to contribute to the development of students' creativity, motivation, and critical thought, encouraging also their capability of solving problems in a more collaborative way [30]. The increasing digital research in today's classrooms has encouraged a recent development of specific computer-based approaches for their application in E-learning environments, such as classroom activity detection [21], hand-rising gesture recognition [23], and classroom motion tracking [15]. Particularly, the proliferation of sensors in classrooms has created an environment in which students' behaviours are continuously monitored and recorded [2,28].…”

Section: Introductionmentioning

confidence: 99%

Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

Song

Qian

Chen

et al. 2021

2021 5th International Conference on Digital Signal Processing

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A Two-stage Method For Hand-Raising Gesture Recognition in Classroom

Cited by 17 publications

References 11 publications

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection

Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement

Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

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