Examinando por Autor "Santos, Juan Miguel"
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Artículo de Publicación Periódica A control strategy for a tethered follower robot for pulmonary rehabilitation(2020-12-03) Bianchi, Luciano Gustavo; Buniak, Esteban Alejandro; Ramele, Rodrigo; Santos, Juan Miguel"Patients that suffer Chronic Obstructive Pulmonary Disease (COPD) undergo a procedure called Pulmonary Rehabilitation that helps them to improve disease prognosis. Pulmonary Rehabilitation consists of different physical exercises and walking activities conducted at medical facilities under supervision of a physical therapist. In order to perform these procedures, patients require oxygen assistance, but the oxygen tank cannot be carried by the patient due to the musculoskeletal atrophy that characterize this pathology and external assistance is required. The assistance to transport the bulky oxygen tank can be provided by a robotic device that follows the patient while performing the physical activities. This work provides an initial study on the controlling mechanism of a differential tethered robot that implements a leader-follower configuration to carry the oxygen tank for these procedures. Two alternative control strategies are proposed. Results on a simulated and on a real prototype confirms the feasibility of the proposed solution."Ponencia en Congreso Discovering sensing capability in multi-agent systems(2010) Parpaglione, María Cristina; Santos, Juan Miguel"What should be the sensing capabilities of agents in a Multi-Agent System be to solve a problem efficiently, quickly and economicly? This question often appears when trying to solve a problem using Multi-Agent Systems. This paper introduces a method to find these sensing capabilities in order to solve a given problem. To achieve this, the sensing capability of an agent is modeled by a parametrized function and then Genetic Algorithms are used to find the parameters’ values. The individual behavior of the agents are found with Reinforcement Learning."Artículo de Publicación Periódica EEG waveform analysis of P300 ERP with applications to brain computer interfaces(2018-11) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel"The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition."Artículo de Publicación Periódica EPOC Emotiv EEG Basics(2022-10) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan MiguelThis document provides some basic guidance to start working with the EPOC Emotiv neuroheadset device and describes how to use it to perform basic Brain-Computer Interface (BCI) research. A brief tutorial on how to set up the device, from its electrophysiological point of view, as well as a description and practical code to perform some basic analysis, is explained. A basic experiment is introduced to detect one of the oldest and, indeed, quite still valuable electrophysiological correlate, visual occipital alpha waves, or Berger Rhythm. An additional experiment is expounded where the power spectrum of alpha waves is reduced when a subject is affected by background cognitive disturbances. This document also briefs about the extraction of information by using the EPOC Emotiv library and also with python Emokit package. This report presents a basic guide on how to use EEGLAB + MATLAB, as well as python stack to perform the neurophysiological analysis. Finally, a basic analysis on different feature extraction and classification methods is provided.Ponencia en Congreso Equilivest: A robotic vest to aid in post-stroke dynamic balance rehabilitation(2023-01) Paviotti, Franco; Buniak, Esteban; Ramele, Rodrigo; Freixes, Orestes; Santos, Juan MiguelBrain stroke is a devastating medical condition, that affects world population and is the main cause of disabilities worldwide. Disabilities related to stroke can affect motor pathways, and may lead to several motor function disorders. One important aspect of motor function is balance which is the ability to control the body’s center of mass inside the base support provided by the lower limb. Stroke can affect dynamic balance as well, which is manifested while walking, impairing autonomy and independence, important factors in Activities of Daily Living (ADL) particularly for young patients.Artículo de Publicación Periódica Histogram of gradient orientations of signal plots applied to P300 detection(2019-07) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel"The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."Artículo de Publicación Periódica Training a gaming agent on brainwaves(2020-12-07) Bartolomé, Francisco; Moreno, Juan; Navas, Natalia; Vitali, José; Ramele, Rodrigo; Santos, Juan Miguel"Error-related potential (ErrP) are a particular type of Event-Related Potential (ERP) elicited by a person attending a recognizable error. These Electroencephalographic (EEG) signals can be used to train a gaming agent by a Reinforcement Learning (RL) algorithm to learn an optimal policy. The experimental process consists of an observational human critic (OHC) observing a simple game scenario while their brain signals are captured. The game consists of a grid, where a blue spot has to reach a desired target in the fewest amount of steps. Results show that there is an effective transfer of information and that the agent successfully learns to solve the game efficiently, from the initial 97 steps on average required to reach the target to the optimal number of 8 steps. Our results are expressed in threefold: (i) the mechanics of a simple grid-based game that can elicit the ErrP signal component, (ii) the verification that the reward function only penalizes wrong steps, which means that type II error (not properly identifying a wrong movement) does not affect significantly the agent learning process; (iii) collaborative rewards from multiple observational human critics can be used to train the algorithm effectively and can compensate low classification accuracies and a limited scope of transfer learning schemes."