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- Publisher Website: 10.1080/01691864.2013.869482
- Scopus: eid_2-s2.0-84891834036
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Article: HMM-based state classification of a user with a walking support system using visual PCA features
Title | HMM-based state classification of a user with a walking support system using visual PCA features |
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Authors | |
Keywords | human state classification walking support systems PCA feature extraction hidden Markov models |
Issue Date | 2014 |
Citation | Advanced Robotics, 2014, v. 28, n. 4, p. 219-230 How to Cite? |
Abstract | The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the users motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called "RT Walker" equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition. © 2013 Taylor & Francis and The Robotics Society of Japan. |
Persistent Identifier | http://hdl.handle.net/10722/302911 |
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 0.605 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Taghvaei, Sajjad | - |
dc.contributor.author | Kosuge, Kazuhiro | - |
dc.date.accessioned | 2021-09-07T08:42:50Z | - |
dc.date.available | 2021-09-07T08:42:50Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Advanced Robotics, 2014, v. 28, n. 4, p. 219-230 | - |
dc.identifier.issn | 0169-1864 | - |
dc.identifier.uri | http://hdl.handle.net/10722/302911 | - |
dc.description.abstract | The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the users motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called "RT Walker" equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition. © 2013 Taylor & Francis and The Robotics Society of Japan. | - |
dc.language | eng | - |
dc.relation.ispartof | Advanced Robotics | - |
dc.subject | human state classification | - |
dc.subject | walking support systems | - |
dc.subject | PCA feature extraction | - |
dc.subject | hidden Markov models | - |
dc.title | HMM-based state classification of a user with a walking support system using visual PCA features | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/01691864.2013.869482 | - |
dc.identifier.scopus | eid_2-s2.0-84891834036 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 219 | - |
dc.identifier.epage | 230 | - |
dc.identifier.eissn | 1568-5535 | - |
dc.identifier.isi | WOS:000329155000002 | - |