Recognition of Gait Patterns Using Support Vector Machines

2011 ◽  
pp. 243-262 ◽  
Author(s):  
Rezaul Begg ◽  
Marimuthu Palaniswami
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Gait Pattern ◽  
Tool Support ◽  
Support Vector ◽  
Gait Patterns ◽  
Gait Characteristics ◽  
Vector Machines ◽  
Recognition Technique ◽  
Recognition Capability

Automated gait pattern recognition capability has many advantages. For example, it can be used for the detection of at-risk or faulty gait, or for monitoring the progress of treatment effects. In this chapter, we first provide an overview of the major automated techniques for detecting gait patterns. This is followed by a description of a gait pattern recognition technique based on a relatively new machine-learning tool, support vector machines (SVM). Finally, we show how SVM technique can be applied to detect changes in the gait characteristics as a result of the ageing process and discuss their suitability as an automated gait classifier.

Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines

2009 ◽  
Vol 119 (1-2) ◽  
pp. 32-38 ◽  
Author(s):  
Paula Martiskainen ◽  
Mikko Järvinen ◽  
Jukka-Pekka Skön ◽  
Jarkko Tiirikainen ◽  
Mikko Kolehmainen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Three Dimensional ◽  
Behaviour Pattern ◽  
Support Vector ◽  
Vector Machines

Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps

2011 ◽  
Vol 30 (5) ◽  
pp. 966-975 ◽  
Author(s):  
Daniel Janssen ◽  
Wolfgang I. Schöllhorn ◽  
Karl M. Newell ◽  
Jörg M. Jäger ◽  
Franz Rost ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Self Organizing Maps ◽  
Gait Patterns ◽  
Vector Machines ◽  
Self Organizing

scholarly journals An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

2022 ◽  
Author(s):  
Jianning Wu ◽  
Qiaoling Tan ◽  
Xiaoyan Wu
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Nonlinear Dynamic ◽  
Gait Pattern ◽  
Gait Variability ◽  
Gait Patterns ◽  
Generalization Performance ◽  
Dynamic Correlation ◽  
Gait Characteristics ◽  
Deep Learning Model

Abstract Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.

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