Gait episode identification based on wavelet feature clustering of spectrogram images
Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-deco...
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| container_volume | 2012 |
| creator | Yuwono, M. Su, S. W. Moulton, B. D. Nguyen, H. T. |
| description | Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. Signals from a chest-worn inertial measurement unit (IMU) is processed using Explicit Complementary Filter (ECF) to estimate and track torso angle. Using the feature obtained from wavelet decomposition of spectrogram images, we use an Augmented Radial Basis Neural Network (ARBF) to classify walking episodes. Cluster centroids of ARBF are optimized using Rapid Cluster Estimation (RCE). A pilot study of 11 participants suggests that our approach is able to distinguish between walk and non-walk activities with up to 85.71% sensitivity and 91.34% specificity. |
| doi_str_mv | 10.1109/EMBC.2012.6346582 |
| format | Conference Proceeding |
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W.</creatorcontrib><creatorcontrib>Moulton, B. D.</creatorcontrib><creatorcontrib>Nguyen, H. T.</creatorcontrib><title>Gait episode identification based on wavelet feature clustering of spectrogram images</title><title>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>EMBC</addtitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><description>Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. Signals from a chest-worn inertial measurement unit (IMU) is processed using Explicit Complementary Filter (ECF) to estimate and track torso angle. Using the feature obtained from wavelet decomposition of spectrogram images, we use an Augmented Radial Basis Neural Network (ARBF) to classify walking episodes. Cluster centroids of ARBF are optimized using Rapid Cluster Estimation (RCE). 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T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yuwono, M.</au><au>Su, S. W.</au><au>Moulton, B. D.</au><au>Nguyen, H. T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Gait episode identification based on wavelet feature clustering of spectrogram images</atitle><btitle>2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>EMBC</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2012-01-01</date><risdate>2012</risdate><volume>2012</volume><spage>2949</spage><epage>2952</epage><pages>2949-2952</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>1424441196</isbn><isbn>9781424441198</isbn><eisbn>9781457717871</eisbn><eisbn>1457717875</eisbn><abstract>Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. 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| identifier | ISSN: 1094-687X |
| ispartof | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, Vol.2012, p.2949-2952 |
| issn | 1094-687X 1557-170X 1558-4615 |
| language | eng |
| recordid | cdi_ieee_primary_6346582 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithms Discrete wavelet transforms Gait - physiology Humans Image resolution MATLAB Neural Networks (Computer) Noise measurement Rotation measurement Spectrogram |
| title | Gait episode identification based on wavelet feature clustering of spectrogram images |
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