Browsing by Author "Sarria Paja, Milton Orlando"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Movements classification system for transhumeral amputees using myoelectric signals(Elsevier Ltd, 2024) Arcos Hurtado, Edgar Francisco; Bermeo Varón, Leonardo Antonio; Sarria Paja, Milton Orlando; Azcarate Carmona, Jaime Andrés; Sarria Durán, Juan Camilo; Villarejo Mayor, John JairoThe extent of loss of functionality in people with transhumeral amputation or elbow disarticulation is considered highly compromised. The person with this impairment loses the primary muscle involved in the movement of the hand and fingers, and the elbow flexion/extension. In this sense, the development of myoelectric prostheses must include mechanisms that replicate these movements. Moreover, the control system for controlling a prosthesis must be robust and intuitive, where pattern recognition and artificial intelligence techniques represent an important and increasingly explored alternative. This study aims to develop a movement intention classification system based on myoelectric signals (MES) obtained from an elbow disarticulation amputee. Different features were extracted from the MES as the Mean absolute value (MAV), zero crossings (ZC), slope sign changes (SSC), waveform length (WL), and autoregressive (AR) coefficients. Machine learning classifiers (Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests (RF), and Multilayer Perceptron (MLP)) were used to predict seven gestures: elbow flexion (EF), elbow extension (EE), forearm pronation (FP), forearm supination (FS), hand opening (HO), hand closing (HC), and rest (R). The results obtained with LDA, QDA, KNN, MLP, SVM, and RF were 60.84%, 71.05%, 77.10%, 78.06%, 78.57%, and 79.72% of accuracy, respectively. These results are promising and will allow the development of a myoelectric prosthesis control system for people with transhumeral amputation or elbow disarticulation.Item Sistema computacional para el diagnóstico de la enfermedad de Parkinson empleando el análisis de señales de voz(Universidad Santiago de Cali, 2019) Moofarry Villaquiran, Jhon Fredy; Sarria Paja, Milton OrlandoParkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. This disorder mainly affects older adults at a rate of about 2%, and about 89% of people diagnosed with PD also have speech disorders. This has led to the development of different research in voice processing for Parkinson's patients, which allows not only a diagnosis of the pathology but also a follow-up of its evolution. In recent years, a large number of studies have focused on the automatic detection of pathologies related to the voice, in order to make objective evaluations of the voice in a non-invasive manner. In cases where the pathology primarily affects the vibratory patterns of vocal folds such as Parkinson's, the analyses typically performed are sustained vowel pronunciations. In this article, it is proposed to use information from slow and rapid variations in voice signals, also known as modulating components, combined with an effective feature reduction approach that will be used as input to the classification system. The proposed approach achieves success rates higher than 88%, surpassing the classical approach based on cepstrales coefficients on the Mel scale (MFCC), this was achieved by extracting characteristics in voice records from a Spanish language database, consequent to this was organized into a vector of characteristics and subsequent estimation was made to the diagnosis of the pathology. The results show that the information extracted from components with slow and fast variations is highly discriminatory to support the assisted diagnosis of PD. This information can also be used as a complement to existing systems.