2016
DOI: 10.1016/j.clinbiomech.2016.02.008
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Computer aided analysis of gait patterns in patients with acute anterior cruciate ligament injury

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Cited by 32 publications
(23 citation statements)
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“…Subsequently, the data of each participant and condition were arranged in a row vector to construct a spatio-temporal representation of the gait pattern in a vector space. [4,8,[10][11][12] The waveforms of each marker and spatial direction were concatenated to form the gait pattern vectors with m × 303 dimensions (m markers × 3 spatial directions × 101 points in time). For further analysis, the gait pattern vectors (row vectors) were vertically concatenated to construct an input matrix.…”
Section: Preprocessingmentioning
confidence: 99%
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“…Subsequently, the data of each participant and condition were arranged in a row vector to construct a spatio-temporal representation of the gait pattern in a vector space. [4,8,[10][11][12] The waveforms of each marker and spatial direction were concatenated to form the gait pattern vectors with m × 303 dimensions (m markers × 3 spatial directions × 101 points in time). For further analysis, the gait pattern vectors (row vectors) were vertically concatenated to construct an input matrix.…”
Section: Preprocessingmentioning
confidence: 99%
“…For classification methods, the linear Support Vector Machine (SVM) algorithm has been used in several approaches. [7][8][9][10][11] Other state of the art classifiers such as Ada Boost, [15] Naïve Bayes [14] or Random Forest [16] are capable of solving non-linear classification problems and are commonly considered in machine learning applications.…”
Section: Introductionmentioning
confidence: 99%
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