Visualising gait symmetry/asymmetry from acceleration data
Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to...
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| Veröffentlicht in: | Computer methods in biomechanics and biomedical engineering Jg. 18; H. 9; S. 923 - 930 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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England
Taylor & Francis
04.07.2015
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| ISSN: | 1025-5842, 1476-8259, 1476-8259 |
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| Abstract | Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation. |
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| AbstractList | Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation. Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation.Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation. |
| Author | Yoneyama, Mitsuru |
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| CitedBy_id | crossref_primary_10_1038_s41526_018_0051_2 crossref_primary_10_1016_j_gaitpost_2018_08_037 crossref_primary_10_1186_s12891_022_05211_1 crossref_primary_10_20965_jaciii_2017_p1240 crossref_primary_10_1109_TNSRE_2015_2477856 crossref_primary_10_1016_j_clineuro_2019_105385 crossref_primary_10_1016_j_jns_2015_09_004 |
| Cites_doi | 10.1007/s00221-007-0955-7 10.1016/j.gaitpost.2007.09.006 10.1007/978-3-642-03893-8_60 10.1016/S0966-6362(00)00070-9 10.1093/geronj/48.5.M225 10.3390/sym2021135 10.1016/j.jbiomech.2010.12.013 10.1016/S0021-9290(03)00233-1 10.1109/TITB.2010.2047402 |
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| Title | Visualising gait symmetry/asymmetry from acceleration data |
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