A taxonomic overview and pilot study for evaluation of Augmented Reality based posture matching technique using Technology Acceptance Model

Javid Iqbal, Manjit Singh Sidhu

Research output: Contribution to journalConference article

Abstract

Motor skill training, posture matching and Augmented Reality (AR) based learning technology are considered to be the cornerstones of dance learning paradigm. In this paper, an AR based posture matching technique is presented based upon skeletal mapping and movement matching where each posture is modelled by a sequence of pivotal movements and continuous data frames. An extension of the previously published work by the authors, this paper aims to provide a taxonomic overview of dance learning/ training technologies with respect to existing learning theories. Furthermore, the proposed system is evaluated using the Technology Acceptance Model (TAM) and a pilot study is carried out to assess the acceptability of the system. The results of the pilot study are also utilized for identifying flaws and to calculate the sample size for further evaluation using a larger group of subjects. This research also aims at providing a taxonomical knowledge and categorization of the proposed Augmented Reality Dance Training System (ARDTS) in the state-of-art hierarchical structure of learning theories.

Original languageEnglish
Pages (from-to)345-351
Number of pages7
JournalProcedia Computer Science
Volume163
DOIs
Publication statusPublished - 01 Jan 2019
Event16th International Learning and Technology Conference, L and T 2019 - Jeddah, Saudi Arabia
Duration: 30 Jan 201931 Jan 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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