Browsing by Author "Arcos Hurtado, Edgar Francisco"
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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 Platform for Adaptation of Myoelectric Prostheses in People with Upper Limb Amputation(Springer, 2020-03-03) Arcos Hurtado, Edgar Francisco; Ortegón Sanchez, Andrés Felipe; Rentería, Juberth; Castillo García, Javier Ferney; Millán Castro, Maria Del MarThis paper describes a platform for adaptation of myoelectric prostheses in people with upper limb amputation. The design of the platform is based on the anthropometry and biomechanics of human upper limb, servomotors are used to drive each degree of freedom, except in the articulation of the elbow, in which a gear motor is used. The myoelectric signal acquisition system includes Myoware myoelectric signal sensors from the company Advancer Technologies, an embedded system based on Arduino and a graphic interface to visualize myoelectric signals in real time. The implementation platform allows to replicate flexion/extension movements for the elbow, wrist, and each finger of the hand, pronation/supination of the wrist, and adduction/abduction of the thumb. The data acquisition system allows to visualize in real time, muscular activity concerning for 4 muscles, and was tested in people with upper limb amputation registering significant values for different movement intentions. The platform presented provides a feedback that could improve the adaptation of a superior limb amputee to a myoelectric prosthesis. The characterization of myoelectric signals generated by the residual limb of a person with upper limb amputation, allows to generate control signals according to a movement intention that would be replicated in the platform.