Laboratorio de Bienestar Porcino: Primeros Resultados y Primeras Conclusiones

La puesta en marcha del Laboratorio de Bienestar Porcino comenzó con una iniciativa de un grupo de investigadores de la E.T.S.I. Agrónomos de Madrid (UPM) que, con el apoyo y financiación de la empresa Tragsa, desarrolló un modelo de alojamiento para cebo de cerdos que incluía un sistema patentado de separación in situ de heces y orina

Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.Facultad de Informátic

Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.Facultad de Informátic

Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis. The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.Facultad de Informátic

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI?port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.Agencia Estatal de InvestigaciónInstituto de Salud Carlos II

Laboratorio de Bienestar Porcino: Primeros Resultados y Primeras Conclusiones

La puesta en marcha del Laboratorio de Bienestar Porcino comenzó con una iniciativa de un grupo de investigadores de la E.T.S.I. Agrónomos de Madrid (UPM) que, con el apoyo y financiación de la empresa Tragsa, desarrolló un modelo de alojamiento para cebo de cerdos que incluía un sistema patentado de separación in situ de heces y orina

Coding Prony's method in MATLAB and applying it to biomedical signal filtering

Background:The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS). Results:The code implemented does not achieve 100%-correct signal approximation and, of the methods tested, LS and MPM perform best. When filtering mfVEP records in the Prony domain, the value of the area under the receiver-operating-characteristic (ROC) curve is 0.7055 compared with 0.6538 obtained with the usual filtering method used for this type of signal (discrete Fourier transform low-pass filter with a cut-off frequency of 35 Hz). Conclusions:This paper reviews Prony's method in relation to signal filtering and approximation, provides the MATLAB code needed to implement the classic, LS, TLS and MPM methods, and tests their performance in biomedical signal filtering and function approximation. It emphasizes the importance of improving the computational methods used to implement the various methods described above.Universidad de AlcaláSecretaría de Estado de Investigación, Desarrollo e Innovació