Subject Recognition Based on Ground Reaction Force Measurements of Gait Signals

Moustakidis, Serafeim; Theocharis, John B.; Giakas, Giannis

doi:10.1109/tsmcb.2008.927722

Cited by 65 publications

(36 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In these sensor-based approaches, human action is usually described by the output signal obtained from sensors either attached to the human body or installed in the environment (excluding cameras and depth sensors). In [44], an effective subject recognition approach is designed using GRF measurements of human gait. In [45], Yang et al proposed an action recognition system using wearable motion sensor networks.…”

Section: B Pose-based Approachesmentioning

confidence: 99%

Inverse Dynamics for Action Recognition

Mansur

Makihara²,

Yagi³

2013

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Abstract-Pose-based approaches for human action recognition are attractive owing to their accurate use of human motion information. Traditionally, such approaches used kinematic features for classification. However, in addition to having high dimensions and a small interclass variation, kinematic features do not consider the interaction of the environment on human motion. In this paper, we propose a method for action recognition using dynamic features, derived by applying inverse dynamics to a physics-based representation of the human body. The physics-based model is articulated and actuated with muscles and consists of joints with variable stiffness. Dynamic features under consideration include the torques from the knee and hip joints of both legs and, implicitly, gravity, ground reaction forces, and the pose of the remaining body parts. These features are more discriminative than kinematic features, resulting in a low-dimensional representation for human actions, which preserves much of the information of the original high-dimensional pose. This low-dimensional feature achieves good classification performance even with a relatively small training data set in a simple classification framework such as a hidden Markov model. The effectiveness of the proposed method is demonstrated through experiments on the Carnegie Mellon University motion capture data set and Osaka University Kinect action data set with various actions.

show abstract

Section: B Pose-based Approachesmentioning

confidence: 99%

Inverse Dynamics for Action Recognition

Mansur

Makihara²,

Yagi³

2013

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…However, as we have observed in our previous work 21 , this approach fails when considering highly-dimensional feature spaces, as the ones encountered in hyperspectral remote sensing classification tasks. In order to improve the feature selection characteristics of the learning process, we proposed the inclusion of deterministic information in the REA, based on the notion of the so-called feature partition vector 36 (FPV). The FPV is a criterion that quantifies the degree to which each example of a set of Q labeled patterns can be correctly classified by a single feature, independently from all other features, and is defined -considering the ith feature -through , that can be derived from any classifier capable of producing fuzzy outputs.…”

Section: Reinforcing the Feature Selection Processmentioning

confidence: 99%

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Stavrakoudis¹,

Galidaki²,

Gitas³

et al. 2012

IJCIS

View full text Add to dashboard Cite

This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

show abstract

“…In [28], an effective subject recognition approach is designed using ground reaction force (GRF) measurements of human gait. In [29], Yang et al proposed an action recognition system using wearable motion sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Action recognition using dynamics features

Mansur

Makihara

Yagi

2011

2011 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-In this paper, we propose a method of action recognition using dynamics features based on physics model. The dynamics features are composed of torques from knee and hip joints of both legs and implicitly include the gravity, ground reaction forces, and the pose of the remaining body parts. These features are more discriminative than the kinematics features, and they result in a low dimensional representation of a human action which preserves much information of the original high dimensional pose. This low dimensional feature allows us to achieve a good classification performance even with a relatively small training data in a simple classification framework such as HMM. The effectiveness of the proposed method is demonstrated through experiments on the CMU motion capture dataset with various actions.

show abstract

Subject Recognition Based on Ground Reaction Force Measurements of Gait Signals

Cited by 65 publications

References 19 publications

Inverse Dynamics for Action Recognition

Inverse Dynamics for Action Recognition

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Action recognition using dynamics features

Contact Info

Product

Resources

About