A framework for correcting human motion alignment for traditional dance training using augmented reality
This paper presents a framework for motion capture analysis for dance learning technology using Microsoft Kinect V2. The proposed technology utilizes motion detection, emotion analysis, coordination analysis and interactive feedback techniques for a particular dance style selected by the trainee...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/20029/1/KMICe2016%2059%2063.pdf |
| _version_ | 1825804293904531456 |
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| author | Iqbal, Javid Sidhu, Manjit Singh |
| author_facet | Iqbal, Javid Sidhu, Manjit Singh |
| author_sort | Iqbal, Javid |
| collection | UUM |
| description | This paper presents a framework for motion capture
analysis for dance learning technology using Microsoft Kinect V2. The proposed technology
utilizes motion detection, emotion analysis,
coordination analysis and interactive feedback
techniques for a particular dance style selected by the trainee.This motion capture system solves the
heterogeneity of the existing dance learning system
and hence provides robustness. The analysis of the
proposed work is carried out using query techniques
and heuristic evaluation. The Microsoft Kinect V2
embedded with Augmented Reality (AR) technology
is explored to demonstrate the recognition accuracy
of the proposed framework. |
| first_indexed | 2024-07-04T06:12:13Z |
| format | Conference or Workshop Item |
| id | uum-20029 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:12:13Z |
| publishDate | 2016 |
| record_format | eprints |
| spelling | uum-200292016-11-24T01:36:40Z https://repo.uum.edu.my/id/eprint/20029/ A framework for correcting human motion alignment for traditional dance training using augmented reality Iqbal, Javid Sidhu, Manjit Singh QA75 Electronic computers. Computer science This paper presents a framework for motion capture analysis for dance learning technology using Microsoft Kinect V2. The proposed technology utilizes motion detection, emotion analysis, coordination analysis and interactive feedback techniques for a particular dance style selected by the trainee.This motion capture system solves the heterogeneity of the existing dance learning system and hence provides robustness. The analysis of the proposed work is carried out using query techniques and heuristic evaluation. The Microsoft Kinect V2 embedded with Augmented Reality (AR) technology is explored to demonstrate the recognition accuracy of the proposed framework. 2016-08-29 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/20029/1/KMICe2016%2059%2063.pdf Iqbal, Javid and Sidhu, Manjit Singh (2016) A framework for correcting human motion alignment for traditional dance training using augmented reality. In: Knowledge Management International Conference (KMICe) 2016, 29 – 30 August 2016, Chiang Mai, Thailand. http://www.kmice.cms.net.my/kmice2016/files/KMICe2016_eproceeding.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Iqbal, Javid Sidhu, Manjit Singh A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title | A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title_full | A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title_fullStr | A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title_full_unstemmed | A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title_short | A framework for correcting human motion alignment for traditional dance training using augmented reality |
| title_sort | framework for correcting human motion alignment for traditional dance training using augmented reality |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/20029/1/KMICe2016%2059%2063.pdf |
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