Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas.

A Survey of Glove-Based Systems and Their Applications

Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not sufficient for a full understanding of sign language communication. Nonmanual signals and grammatical processes which result in systematic variations in sign appearance are integral aspects of this communication but have received comparatively little attention in the literature. In this survey, we examine data acquisition, feature extraction and classification methods employed for the analysis of sign language gestures. These are discussed with respect to issues such as modeling transitions between signs in continuous signing, modeling inflectional processes, signer independence, and adaptation. We further examine works that attempt to analyze nonmanual signals and discuss issues related to integrating these with (hand) sign gestures. We also discuss the overall progress toward a true test of sign recognition systems?dealing with natural signing by native signers. We suggest some future directions for this research and also point to contributions it can make to other fields of research. Web-based supplemental materials (appendicies) which contain several illustrative examples and videos of signing can be found at www.computer.org/publications/dlib.

/pdf/automatic-sign-language-analysis-a-survey-and-the-future-3yh2fbjd5b.pdf

Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Data Mining and Machine Learning in Astronomy

This paper presents an early exploration of the suitability of the Leap Motion controller for Australian Sign Language (Auslan) recognition. Testing showed that the controller is able to provide accurate tracking of hands and fingers, and to track movement. This detection loses accuracy when the hand moves into a position that obstructs the controller's ability to view, such as when the hand rotates and is perpendicular to the controller. The detection also fails when individual elements of the hands are brought together, such as finger to finger. In both of these circumstances, the controller is unable to read or track the hand. There is potential for the use of this technology for recognising Auslan, however further development of the Leap Motion API is required.

/pdf/the-leap-motion-controller-a-view-on-sign-language-mdchx33s54.pdf

The Leap Motion controller: a view on sign language

Neural networks for predicting compressive strength of structural light weight concrete

Recognition of sign language gestures using neural networks

Hubble classification of galaxies using neural networks

The cascade-correlation architecture which is designed for classification tasks has been studied to see if it can be modified to perform function evaluation and interpolation tasks.

Function evaluation and the cascade-correlation architecture

Missing or incomplete data, a common reality, causes problems for artificial neural networks. In this paper we investigate several methods for dealing with missing values in feedforward networks. Reduced networks, substitution, estimation and expanded networks are applied to three data sets. We find that data sets vary in their sensitivity to missing values, and that reduced networks and estimation are the most effective ways of dealing with them.

/pdf/techniques-for-dealing-with-missing-values-in-feedforward-uffdk1q5lr.pdf

Techniques for Dealing with Missing Values in Feedforward Networks

Cascade-correlation produces networks with all possible feedforward connections. The pruning of redundant connections using a simple saliency measure is examined, along with three methods for stopping the pruning process which may be used with any network type. The methods are shown to be effective and it is further shown that a large number of redundant connections may be located and removed from cascade-correlation networks.

Pruning within cascade-correlation

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