Wyniki wyszukiwania - BazTech

1

Hand posture recognition using modified ensemble of shape functions and global radius-based surface descriptor

Warchoł D.

Computer Science

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2018

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Vol. 19 (2)

115--137

EN

This paper presents an approach to the recognition of static hand gestures based on data acquired from 3D cameras and point cloud descriptors: Ensemble of Shape Functions and Global Radius-based Surface Descriptor. We describe a recognition algorithm consisting of hand segmentation, noise removal and downsampling of point clouds, dividing point cloud bounding boxes to cells, feature extraction and normalization, and gesture classification. Modifications to the descriptors are proposed in order to increase the hand posture recognition rates while decreasing the quantity of used features as well as the computational cost of the algorithm. Experiments performed on four challenging datasets using cross-validation tests prove the usefulness of our approach.

2

A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

Shi W. T., Lyu Z. J., Tang S. T., Chia T. L., Yang C. Y.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

126--135

EN

A bionic hand with fine motor ability could be a favorable option for replacing the human hand when performing various operations. Myoelectric control has been widely used to recognize hand movements in recent years. However, most of the previous studies have focused on whole-hand movements, with only a few investigating subtler motions. The aim of this study was to construct a prototype system for recognizing hand postures with the aim of controlling a bionic hand by analyzing sEMG signals measured at the flexor digitorum superficialis and extensor digitorum muscles. We adopted multiple features commonly used in previous studies—mean absolute value, zero crossing, slope sign change, and waveform length—in the algorithm for extracting hand-posture features, and the k-nearest-neighbors (KNN) algorithm as the classifier to perform hand-posture recognition. The bionic hand was controlled by an Arduino microprocessor, which converted the signals received from the classification process that were fed to the servo motors controlling the bionic fingers. We constructed a two-channel sEMG pattern-recognition system that can identify human hand postures and control a homemade bionic hand to perform corresponding hand postures. The KNN approach was able to recognize four different hand postures with a classification accuracy of 94% in the online experiment by using the channel combination. Moreover, the experimental tests show that the bionic hand could faithfully imitate the hand postures of the human hand. This study has bridged the gap between the features of sEMG signals of fingers and the postures of a bionic hand.

3

Recognition of Hand Posture Based on a Point Cloud Descriptor and a Feature of Extended Fingers

Warchoł D., Wysocki M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2016

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Vol. 10, No. 1

48--57

EN

Our work involves hand posture recognition based on 3D data acquired by the KinectTM sensor in the form of point clouds. We combine a descriptor built on the basis of the Viewpoint Feature Histogram (VFH) with additional feature describing the number of extended fingers. First, we extract a region corresponding to the hand and then a histogram of the edge distances from the palm center is built. Based on quantized version of the histogram we calculate the number of extended fingers. This information is used as a first feature describing the hand which, together with VFH-based features, form the feature vector. Before calculating VFH we rotate the hand making our method invariant to hand rotations around the axis perpendicular to the camera lens. Finally, we apply nearest neighbor technique for the posture classification. We present results of crossvalidation tests performed on a representative dataset consisting of 10 different postures, each shown 10 times by 10 subjects. The comparison of recognition rate and mean computation time with other works performed on this dataset confirms the usefulness of our approach.

4

On the generation of graph representation of hand postures for syntactic pattern recognition

Myśliński Sz.

Journal of Applied Computer Science

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2009

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Vol. 17, nr 1

71-84

EN

The results of research into construction of a method that generates an IE-graph [8] representation of hand postures are presented in the paper. The method allows one to represent hand postures of the Polish Sign Language with a class of graphs that can be parsed with an efficient ETPL(k) graph syntax analyzer introduced in [6].

5

Constrained contour matching in hand posture recognition

Wilkowski A., Kasprzak W.

Image Processing & Communications

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2009

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Vol. 14, no 2-3

31-41

EN

In this paper constrained contour models are applied for hand posture-recognition in single color images. In particular, the proposed algorithm utilizes a class of physics-based modelling methods called Deformable Templates [1],[2],[3]. After color-based image segmentation a contour hypothesis is detected and some features are extracted, suitable for comparison with the template's geometric properties. Several metrics for matching contour templates against image data are discussed. The described methods are evaluated experimentally and referred to a known hand posture recognition algorithm.