Mixture Model Segmentation for Gait Recognition

Field, Matthew; Stirling, David; Naghdy, Fazel; Pan, Zengxi

doi:10.1109/lab-rs.2008.26

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In Field et al (2008a), it was shown that removing less abundant clusters from the GMM hinder identification of the activity and is detrimental to a stable centre of mass trajectory in possible robot motions. In Field et al (2008b), recognisable behaviour was segmented with a range of techniques and compared to subjective segmentations.…”

Section: Experimental Results In Human Motion Recognitionmentioning

confidence: 99%

“…Current methodologies in handling the data are generally limited in their scope and application. There are promising and relatively new methods which may expedite future research outcomes including extensions on stochastic machine learning algorithms such as some hierarchical HMM (Kulic et al, 2008) topologies or restricted Boltzmann machines (Taylor and Hinton, 2009) and semi-supervised approaches (Field et al, 2008a;Zhou et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Human motion capture sensors and analysis in robotics

Field

Pan

Stirling

et al. 2011

Industrial Robot: An International Journal

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Purpose -The purpose of this paper is to provide a review of various motion capture technologies and discuss the methods for handling the captured data in applications related to robotics. Design/methodology/approach -The approach taken in the paper is to compare the features and limitations of motion trackers in common use. After introducing the technology, a summary is given of robotic-related work undertaken with the sensors and the strengths of different approaches in handling the data are discussed. Each comparison is presented in a table. Results from the author's experimentation with an inertial motion capture system are discussed based on clustering and segmentation techniques. Findings -The trend in methodology is towards stochastic machine learning techniques such as hidden Markov model or Gaussian mixture model, their extensions in hierarchical forms and non-linear dimension reduction. The resulting empirical models tend to handle uncertainty well and are suitable for incrementally updating models. The challenges in human-robot interaction today include expanding upon generalising motions to understand motion planning and decisions and build ultimately context aware systems. Originality/value -Reviews including descriptions of motion trackers and recent methodologies used in analyzing the data they capture are not very common. Some exist, as has been pointed out in the paper, but this review concentrates more on applications in the robotics field. There is value in regularly surveying the research areas considered in this paper due to the rapid progress in sensors and especially data modeling.

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Section: Experimental Results In Human Motion Recognitionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Human motion capture sensors and analysis in robotics

Field

Pan

Stirling

et al. 2011

Industrial Robot: An International Journal

Self Cite

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show abstract

“…Apart from this, motioning the induced symbolic patterns also provides a diagnostic ability guiding the often cyclic and interactive nature of applying machine learning in general. Previous other studies have validated this approach by combining together with unsupervised mixture modelling for gait recognition (Field et al, 2008) (Hesami et al, 2008). The premise of this proposed work is that all humans have, by the stage of adolescence (or maturity) developed various stylistic signatures or patterns of motion behaviour that can be typically (uniquely) associated with an individual.…”

Section: Wwwintechopencommentioning

confidence: 95%

Symbolic Modelling of Dynamic Human Motions

Stirling¹,

Hesami²,

Ritz³

et al. 2010

Biosensors

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“…Methodologies investigated to this point follow heuristic classifiers, Gaussian mixture models (GMM) [16], support vector machines [12], and hidden Markov model (HMM) [17,18]. However, those approaches consider a fixed number of human motions.…”

Section: Introductionmentioning

confidence: 99%

Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

Ahmed

Song

2012

Sensors

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In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.

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Mixture Model Segmentation for Gait Recognition

Abstract: Abstract

Cited by 7 publications

References 13 publications

Human motion capture sensors and analysis in robotics

Human motion capture sensors and analysis in robotics

Symbolic Modelling of Dynamic Human Motions

Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

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