The abundance of computing and mobile devices makes the problem of user identification and verification an essential requirement for many applications. Haptics devices include the sense of touch in the form of kinesthetic and tactile feedback which provide additional features within handwritten signatures. However, they generate high dimensional data and dimensionality reduction techniques become useful for data mining, machine learning and visualization. Nonlinear transformations have been used for this, but in present day scenarios (Big Data, the Internet of Things, massive data streams, etc.) the computation becomes more complex, time consuming or impractical. Moreover, the relationships between the features of the original and the target spaces are more difficult to uncover. Extreme Learning Machines (ELM) are used for approximating nonlinear manifold learning methods in two ways: as a functional representation for implicit methods, and as simpler surrogate models for explicit mapping techniques. In the context of Haptic handwritten signatures, five implicit and explicit nonlinear transformation methods are investigated. In all cases it was found that ELM approximations to the mappings obtained with the original methods exhibit very good behavior and can be used either as functional representations for the implicit methods or as simpler surrogate models for explicit techniques.

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k -nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).

In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favorably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.

This paper explores the use of rough set theory for feature selection in high dimensional haptic-based handwritten signatures (exploited for user identification). Two rough set-based methods for feature selection are analyzed, the first is a greedy approach while the second relies on genetic algorithms to find minimal subsets of attributes. Also, to further reduce the haptic feature space while maximizing user identification accuracy, a method is proposed where feature vectors are subsampled prior to the feature selection procedure. Rough set-generated minimal subsets are initially exploited to determine the importance of different haptic data types (e.g. force, position, torque and orientation) in discriminating between different users. In addition, a comparison between rough set-based methods and classical machine learning techniques in the selection of minimal information-preserving subsets of features in high dimensional haptic datasets, is provided. The criteria for comparison are the length of the selected subsets of features and their corresponding discrimination power. Support Vector Machine classifiers are used to evaluate the accuracy of the selected minimal feature vectors. The results demonstrated that the combination of rough set and genetic algorithm techniques can outperform well-established machine learning methods in the selection of minimal subsets of features present in haptic-based handwritten signatures.

Undesired physical fatigue reduces the overall Quality of Experience (QoE) of virtual reality haptics applications. Detecting fatigue is the first step in rectifying this problem. Fatigue in usability analysis is usually detected through conducting questionnaires and observations. This paper introduces an objective indirect discovery of user's fatigue through analyzing data of a haptic writing application. Our results show that if users are feeling tired their kinetic energy would decrease. We can compute this kinetic energy from the velocity of the arm movement during the usage of the haptic device.

In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.