Ontological Framework of Arm Gesture Information for the Human Upper Body

Khairunizam, Wan; Ikram, K.; Bakar, Shahriman Abu; Razlan, Z. M.; Zunaidi, I.

doi:10.1007/978-981-10-8788-2_45

Cited by 11 publications

(1 citation statement)

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“…Previously, most of the researchers focus on investigating various type of sensors to record the upper body movement including the arm. The various features from arm movements are extracted and investigated to increase the accuracy of the decision-making systems to recognize human gesture [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of attribute domain for geometrical gesture performed by arm movements

Khairunizam

Ikram

Halim

et al. 2019

IJEECS

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<span>Hand gesture recognition commonly uses a camera to track hand movements and transformed into gesture database by using various computational approaches. Motion tracking utilized to map coordinate point of the subject movement, either in skeletal model or marker tracing. Data from motion trackers usually contains massive coordinate sequences of marker movement. A reliable method is required to select best features and analyze these data. However, the current issue whether the selected features and data presentation are significant for the research or not. This research brings the concept of ontology design for arm gesture recognition systems by utilizing the motion capture system. Ontology is the conceptual structure mainly used to retrieve information by establishing relation in complex data model. The proposed ontology framework is divided into three domains which are knowledge domain, attribute domain and process domain. Knowledge domain holds pre-processed gestural data from motion capture. The attribute domain is that the level where all the attribute elements were presented. This paper shows the analysis of the datasets in attribute domain. The analysis is divided into two parts which is precision measure and ANOVA test. Both analyses are to prove the reliability of datasets in attribute domain. The precision measure is used to remove all the common data for all gesture. A statistical analysis of p-value is lower than 0.01 which means the gestural data are statistically significant to be used for the similarity measure.</span>

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