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- Publisher Website: 10.1109/CYBER.2018.8688122
- Scopus: eid_2-s2.0-85064968199
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Conference Paper: Sensing and Data Analysis for Assessing Human Balance Ability
Title | Sensing and Data Analysis for Assessing Human Balance Ability |
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Authors | |
Issue Date | 2018 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486 |
Citation | 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Tianjin, China, 19-23 July 2018, p. 807-812 How to Cite? |
Abstract | This paper presents the development of a novel balance sensor capable of assessing human balance ability. This sensor can effectively detect the tread force distribution information under human feet with high resolution and frame rate while they are trying to balance themselves. The principle and performance of the sensor are discussed in the paper. We have used this sensor to capture tread force distribution variation videos from 6 categories of different human balancing actions. A novel 3D convolutional neural network is developed to classify these 6 actions. The high accuracy of video classifications have been achieved using 3D CNN. In addition, our preliminary experimental results demonstrates that applying deep learning method, this sensor is able to recognize different human motion patterns. Ongoing work is to exploit its potential use in neural disease diagnosis. |
Persistent Identifier | http://hdl.handle.net/10722/282991 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Wang, S | - |
dc.contributor.author | Xi, N | - |
dc.date.accessioned | 2020-06-05T06:23:49Z | - |
dc.date.available | 2020-06-05T06:23:49Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Tianjin, China, 19-23 July 2018, p. 807-812 | - |
dc.identifier.isbn | 978-1-5386-7058-3 | - |
dc.identifier.uri | http://hdl.handle.net/10722/282991 | - |
dc.description.abstract | This paper presents the development of a novel balance sensor capable of assessing human balance ability. This sensor can effectively detect the tread force distribution information under human feet with high resolution and frame rate while they are trying to balance themselves. The principle and performance of the sensor are discussed in the paper. We have used this sensor to capture tread force distribution variation videos from 6 categories of different human balancing actions. A novel 3D convolutional neural network is developed to classify these 6 actions. The high accuracy of video classifications have been achieved using 3D CNN. In addition, our preliminary experimental results demonstrates that applying deep learning method, this sensor is able to recognize different human motion patterns. Ongoing work is to exploit its potential use in neural disease diagnosis. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486 | - |
dc.relation.ispartof | IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) | - |
dc.rights | IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Copyright © IEEE. | - |
dc.rights | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.title | Sensing and Data Analysis for Assessing Human Balance Ability | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Xi, N: xining@hku.hk | - |
dc.identifier.authority | Xi, N=rp02044 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CYBER.2018.8688122 | - |
dc.identifier.scopus | eid_2-s2.0-85064968199 | - |
dc.identifier.hkuros | 310101 | - |
dc.identifier.spage | 807 | - |
dc.identifier.epage | 812 | - |
dc.publisher.place | United States | - |