AR based upper limb rehabilitation system

Aung, YM; Al-Jumaily, A

AR based upper limb rehabilitation system

Aung, YM Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2012, pp. 213 - 218
Issue Date:: 2012-10-18

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Field	Value	Language
dc.contributor.author	Aung, YM	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2012-10-18	en_US
dc.identifier.citation	Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2012, pp. 213 - 218	en_US
dc.identifier.isbn	9781457711992	en_US
dc.identifier.issn	2155-1774	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22393
dc.description.abstract	More than 62,000 Australians were reported suffered from Traumatic Brain Injury (TBI), Spinal Cord Injury (SCI) and Cerebrovascular Accident (CVA) or stroke in 2011. These injuries and accidents lead to physical disability that yields in limitation for performing a person's daily life activities. To overcome such limitations, physical rehabilitation is conducted which requires one-to-one attention that creates a shortage in therapists and lead to high cost. In this paper, a development of an effective augmented reality (AR) based upper limb rehabilitation system with low cost is presented. Our development aims to close the gap in shortage of therapists, high health care cost of TBI, SCI and stroke. The proposed system can be used at home as well as in rehabilitation centers, units, and hospitals with minimum therapist supervision. It consists of two modules: AR based rehabilitation exercises module and real-time active muscle module. The first module aims to increase the upper limb range of motion via reaching exercises, and strengthen the associated muscles. In second module, the patient's EMG signals were used as an input to monitor the muscle performance in real time during training. Our development had tested with 10 healthy subjects and had demonstrated in Port Kembla Rehabilitation Hospital. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics	en_US
dc.relation.isbasedon	10.1109/BioRob.2012.6290680	en_US
dc.title	AR based upper limb rehabilitation system	en_US
dc.type	Conference Proceeding
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	Rome	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

More than 62,000 Australians were reported suffered from Traumatic Brain Injury (TBI), Spinal Cord Injury (SCI) and Cerebrovascular Accident (CVA) or stroke in 2011. These injuries and accidents lead to physical disability that yields in limitation for performing a person's daily life activities. To overcome such limitations, physical rehabilitation is conducted which requires one-to-one attention that creates a shortage in therapists and lead to high cost. In this paper, a development of an effective augmented reality (AR) based upper limb rehabilitation system with low cost is presented. Our development aims to close the gap in shortage of therapists, high health care cost of TBI, SCI and stroke. The proposed system can be used at home as well as in rehabilitation centers, units, and hospitals with minimum therapist supervision. It consists of two modules: AR based rehabilitation exercises module and real-time active muscle module. The first module aims to increase the upper limb range of motion via reaching exercises, and strengthen the associated muscles. In second module, the patient's EMG signals were used as an input to monitor the muscle performance in real time during training. Our development had tested with 10 healthy subjects and had demonstrated in Port Kembla Rehabilitation Hospital. © 2012 IEEE.

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http://hdl.handle.net/10453/22393