Luca Liparulo

An Accurate Algorithm for the Identification of Fingertips Using an RGB-D Camera

RGB-D cameras and depth sensors have made possible the development of an uncountable number of applications in the field of human-computer interactions. Such applications, varying from gaming to medical, have made possible because of the capability of such sensors of elaborating depth maps of the placed ambient. In this context, aiming to realize a sound basis for future applications relevant to the movement and to the pose of hands, we propose a new approach to recognize fingertips and to identify their position by means of the Microsoft Kinect technology. The experimental results exhibit a really good identification rate, an execution speed faster than the frame rate with no meaningful latencies, thus allowing the use of the proposed system in real time applications. Furthermore, the scored identification accuracy confirms the excellent capability of following also little movements of the hand and it encourages the real possibility of successive implementations in more complex gesture recognition systems.

Fuzzy clustering using the convex hull as geometrical model

A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.

Fuzzy membership functions based on point-to-polygon distance evaluation

In this paper, a new approach is presented for the evaluation of membership functions in fuzzy clustering algorithms. Starting from the geometrical representation of clusters by polygons, the fuzzy membership is evaluated through a suited point-to-polygon distance estimation. Three different methods are proposed, either by using the geometrical properties of clusters in the data space or by using Gaussian or cone-shaped kernel functions. They differ from the basic trade-off between computational complexity and approximation accuracy. By the proposed approach, fuzzy clusters of any geometrical complexity can be used, since there is no longer required to impose constraints on the shape of clusters resulting from the choice of computationally affordable membership functions. The methods illustrated in the paper are validated in terms of speed and accuracy by using several numerical simulations.

Multimedia and Gaming Technologies for Telerehabilitation of Motor Disabilities [Leading Edge]

Rehabilitation for chronic conditions resulting from acute or progressive disease might be delivered in an outpatient facility as in the case of telerehabilitation, self rehabilitation and, more generally, in the context of home-based rehabilitation to improve the patients’ quality of life. Here we present the emerging field of home-based applications for continuous digital health, focusing in particular on low-cost rehabilitation systems for motor disabilities based on multimedia and gaming technologies. Innovative technologies for telerehabilitation are illustrated. We also present recent advances in telerehabilitation, considering the most relevant projects that best represent new trends for research and development of new technologies and applications in this context.

A study on crude oil prices modeled by neurofuzzy networks

In the last decade the increasing volatility of petroleum markets has challenged time series analysts to produce highly predictive models. Crude Oil is a major driver of the global economy and its price fluctuations are a key indicator for producers, consumers and investors. With investors following the longerterm upward trend in Energy prices Commodity investments, we believe this will drive an increasing importance for methodologies like neurofuzzy networks for risk quantification, measurement and management. The data used is Crude Oil prices for both Brent and WTI in the 10 year period from 2001 to 2010. We will prove that the neurofuzzy approach based on ANFIS networks compare favorably with respect to other standard and neural models and it is able to achieve useful performances in terms of accurate prediction of prices and their probability distribution.

Improved online fuzzy clustering based on unconstrained kernels

A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation. Since the performance of on-line algorithms suffers from the pattern presentation order, we also consider the problem of cluster validity aiming at proving the minimal dependence and the robustness with respect to the initialization of inner parameters in the proposed algorithm. The numerical results reported in the paper prove that the proposed approach is able to improve the performances of well-known algorithms on some reference benchmarks.

A higher-order fuzzy neural network for modeling financial time series

This work investigates on the widespread use of fuzzy neural networks in time series forecasting, concerning in particular the energy commodity markets. We propose a new learning strategy suited to any neural model. The proposed approach is further assessed in the case of higher-order Sugeno-type fuzzy rules, which are able to replicate the daily data and to reproduce the same statistical features for various Commodity time series. The data used are obtained from the daily return series of specific energy commodities, such as coal, natural gas, crude oil and electricity, over the period 2001-2010 for both the European and US markets. We will prove that our approach can obtain interesting results in terms of prediction accuracy and volatility estimation, compared to well-known neural and fuzzy neural models and to the ARMA-GARCH statistical paradigm.

A genetic algorithm for feature selection in gait analysis

This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis, which aims at controlling movements of patients affected by neurological diseases. The proposed approach is intended to a feature selection procedure as an optimization strategy based on genetic algorithms, where the misclassification error of healthy/diseased patients is adopted as the fitness function. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Moreover, the technique herein described will provide a useful tool in the context of medical diagnosis. In fact, we will prove that for the classification problem at hand the whole set of features is redundant and it can be significantly pruned. The obtained results on a real dataset acquired in our biomechanics laboratory show a very interesting classification accuracy using six features only among the sixteen acquired by the stereophotogrammetric system.

A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients’ impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.

Time Series Analysis by Genetic Embedding and Neural Network Regression

In this paper, the time series forecasting problem is approached by using a specific procedure to select the past samples of the sequence to be predicted, which will feed a suited function approximation model represented by a neural network. When the time series to be analysed is characterized by a chaotic behaviour, it is possible to demonstrate that such an approach can avoid an ill-posed data driven modelling problem. In fact, classical algorithms fail in the estimation of embedding parameters, especially when they are applied to real-world sequences. To this end we will adopt a genetic algorithm, by which each individual represents a possible embedding solution. We will show that the proposed technique is particularly suited when dealing with the prediction of environmental data sequences, which are often characterized by a chaotic behaviour.