4 research outputs found

    Image set preparation: A platform to prepare a myoelectric signal to train a CNN

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    Derived from the good performance in the classification of surface Electromyography signals using CNN for its application in prosthetics, rehabilitation, and medicine, we present a platform that, from a surface Electromyography, performs the necessary digital processing to generate an image database to train a Convolutional Neural Network. This platform requires inputting the protocol parameters under which the myoelectric signal was acquired. In addition, it allows selection among four groups of Time-Domain features and four types of images that have shown good performance (above 90%) in the current literature. The platform generates images in separate folders for each movement according to the selected parameters. This work offers a valuable tool in classification using surface Electromyography and Convolutional Neural Networks, enabling more efficient customization and optimization of training processes

    Selection of the Best Set of Features for sEMG-Based Hand Gesture Recognition Applying a CNN Architecture

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    The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases

    Development of an acousto-optic system for hyperspectral image segmentation

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    Image segmentation is a typical operation in many image analysis and computer vision applications. However, hyperspectral image segmentation is a field which have not been fully investigated. In this study an analogue-digital image segmentation technique is presented. The system uses an acousto-optic tuneable filter, and a CCD camera to capture hyperspectral images that are stored in a digital grey scale format. The dataset was built considering several objects with remarkable differences in the reflectance and brightness components. In addition, the work presents a semi-supervised segmentation technique to deal with the complex problem of hyperspectral image segmentation, with its corresponding quantitative and qualitative evaluation. Particularly, the developed acousto-optic system is capable to acquire 120 frames through the whole visible light spectrum. Moreover, the analysis of the spectral images of a given object enables its segmentation using a simple subtraction operation. Experimental results showed that it is possible to segment any region of interest with a good performance rate by using the proposed analogue-digital segmentation technique

    Experimental Analysis of Tunable Optical Spectral Imaging System Using a Grating in the Pupil Function

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    Hyperspectral imaging (HSI) systems have been demonstrated as a powerful imaging technique due to their high spectral resolution. HSI can obtain the spectrum for each pixel in the image of a scene, a feature that can be exploited to design optical systems with the purpose of analyzing and characterizing objects and identifying processes within the visible electromagnetic spectrum (bandwidth). In this paper, we present an HSI system comprising a diffraction grating placed in the exit pupil of our optical configuration. The spectrum for each pixel associated with the object appears in the first order of diffraction. We used this system to characterize and tune the required spectral band of the image of the captured object obtaining more information than with an optical imaging system. Accordingly, the proposed optical system is suitable to obtain spectral and hyperspectral imaging at low cost compared to an acousto-optic system or other HSI. The scanning system captures hundreds of spectral images associated with the object, obtaining a maximum spectral resolution of 0.26nm or 260 pm for one of our configurations
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