1,720,979 research outputs found
A smartphone-based application using machine learning for gesture recognition. Using feature extraction and template matching via Hu image moments to recognize gestures
No full textThe rapid development of smart devices, such as smartphones and tablets, leads to new challenges and ushers in a new stage of human-computer interaction. In this context, it becomes essential to develop methods and techniques for a better and more natural interaction with these devices. In this article, we address the problem of gesture segmentation and recognition, taking into account the limited computational resources of smartphone devices. We introduce a methodology for designing efficient and useful applications that, by using low-cost and widely diffused technologies, can be used in telemedicine, home-based rehabilitation, and other biomedical applications for patients with specific disabilities. To this end, we have designed a new machine-learning algorithm that is able to identify hand gestures through the use of Hu image moments, due to their invariance to rotation, translation, scaling, and their low computational cost. The experimental results collected from a case study show an excellent gesture recognition performance and an affordable real-time execution speed on smartphones and other mobile devices
A classification approach to modeling financial time series
No full textIn this paper, several classification methods are applied for modeling financial time series with the aim to predict the trend of successive prices. By using a suitable embedding technique, a pattern of past prices is assigned a class if the variation of the next price is over, under or stable with respect to a given threshold. Furthermore, a sensitivity analysis is performed in order to verify if the value of such a threshold influences the prediction accuracy. The experimental results on the case study of WTI crude oil commodity show a good classification accuracy of the next (predicted) trend, and the best performance is achieved by the K-Nearest Neighbors classification strategy
Selection of clinical features for pattern recognition applied to gait analysis
No full textThis paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. 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 wellknown classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadenceand stride may add useful information for the movement evaluation
TECNICHE DI FUZZY PATTERN RECOGNITION PER L’ELABORAZIONE DI DATI PROVENIENTI DA ELETTROMIOGRAFIA DI SUPERFICIE (sEMG)
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A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction
No full textIn this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of times series generated by complex processes of the real-world. The new learning strategy is suited to any fuzzy inference model, especially in the case of higher-order Sugeno-type fuzzy rules. The data considered herein are real-world cases concerning chaotic benchmarks as well as environmental time series. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying, unknown process with a good prediction of the observed time series
Water quality prediction based on wavelet neural networks and remote sensing
No full textWavelet artificial neural networks and remote sensing techniques can be used to estimate water quality variables such as Chlorophyll-a, turbidity and suspended solids. This paper describes empirical algorithms for the estimation of these variables incorporating information from the Operational Land Imager Sensor on board the Landsat-8 satellite.
Neural networks are seasonally trained using data from the Cefni reservoir (Anglesey, U.K.), covering a variety of physical trophic status. Chlorophyll-a levels and the suspended solids are estimated from the reflectance in band-2 and band-4, while the turbidity values from reflectance in band-4. Experimental results show the potential of Landsat-8 as a substitute of Landsat-7 in water bodies quality monitoring. Moreover, predicted values obtained by using wavelet artificial neural networks fit well measured data and hence, such models provide accurate results therefore improving the efficiency in monitoring water quality parameters and contributing to possible decision making processes in the environmental management
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