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- Title
Method Development Through Landmark Point Extraction for Gesture Classification With Computer Vision and MediaPipe.
- Authors
Suherman, Suherman; Suhendra, Adang; Ernastuti, Ernastuti
- Abstract
40TExamining the physical movements of students during their educational quests holds great significance as these nonverbal cues can exert a substantial influence on academic performance, and boost, learning outcomes, Consequently, numerous researchers are engaged in exploring the domain of gesture categorization employing machine learning techniques.40T 40TInitially, we conducted an observation of students' movements in a virtual learning environment during face-to-face interactions with their teachers. This procedure yielded a roster of thirteen motionbased behaviors, encompassing actions such as tilting the head towards either direction, lowering and lifting the head, as well as gesturing with the right and left hand towards the head and neck area, and positioning the shoulders in a front and lateral direction.40T 40TThis research offers a technique for establishing a set of criteria for categorizing students' gesticulations in online learning by utilizing the comprehensive MediaPipe holistic library and OpenCV to detect, pose and extract salient landmarks. This endeavor culminated in the attainment of a percentage-based metric indicative of gesture identification efficacy pertaining to the aforementioned thirteen motionbased activities.
- Subjects
POINTING (Gesture); NONVERBAL cues; COURSEWARE; COMPUTER vision; STUDENT activism; ONLINE education
- Publication
TEM Journal, 2023, Vol 12, Issue 3, p1677
- ISSN
2217-8309
- Publication type
Article
- DOI
10.18421/TEM123-49