Automatic recognition of gait patterns in human motor disorders using machine learning: A review. (March 2018) - Figueiredo, Joana
Highlights: Automatic recognition of human gait patterns allows an objective and non-invasive analysis of abnormal gait patterns and prescribing task-oriented gait training, by manipulating a massive multidimensional dataset. The kernel principal component analysis and genetic algorithm are promising methods for dimensional reduction of gait parameters due to their ability to work with nonlinear data and converge to a global optimum. A reliable human gait pattern recognition involves the extraction and normalization of gait features, selection of relevant gait features, and classification of the gait patterns. Support vector machines are an accurate tool for recognition of human gait patterns that converges to a global minimum, does not lead to over-fitting, and exhibits an appropriate generalization for new instances. Abstract: Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using "human recognition", "gait patterns'', and "feature selection methods" as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.