Hybrid Human-Machine Interface to Mouse Control for Severely Disabled People

This paper describes a hybrid human-machine interface, based on electro-oculogram (EOG) and electromyogram (EMG), which allows the mouse control of a personal computer using eye movement and the voluntary contraction of any facial muscle. The bioelectrical signals are sensed through adhesives electrodes, and acquired by a custom designed portable and wireless system. The mouse can be moved in any direction, vertical, horizontal and diagonal, by two EOG channels and the EMG signal is used to perform the mouse click action. Blinks are avoided by a decision algorithm and the natural reading of the screen is possible with a specially designed software. A virtual keyboard was used for the experiments with healthy people and with a severely disabled patient. The results demonstrate an intuitive and accessible control, evaluated in terms of performance, time for task execution and user´s acceptance. Besides, a quantitative index to estimate the training impact was computed with good results.Fil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Pérez Berenguer, María Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Bajinay, Sergio. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Zanetti, Roberto. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Valentinuzzi, Maximo. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentin

Muscular synergy classification and myoelectric control using high-order cross-cumulants

High-order statistics (HOS) are well suited for describing non-Gaussian random processes. These techniques are increasingly being employed in myoelectric research, on both time and frequency domain techniques. This work presents HOS-based techniques using only HOS time domain features to classify myoelectric signals. The auto-, cross- and full- (joint) third-order cumulants are evaluated as EMG-signal feature vectors to be compared between them. Four surface EMG signals were processed for classify motions from the upper limbs. Synergy among channels is characterized by the features in both auto and cross modes, and their incidences for classifying five or six movements are analyzed. In contrast to the third-order auto-cumulants, it had been verified that the third-order cross-cumulants have the same classification rate by working with five or six movements. A myoelectric control scheme and its experimental application were executed with normal and disabled subjects, reaching a classification rates of 90%, in average. Accuracy in online experiments was similar to the off-line classification rate.Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Multi-Sensor Embedded System with Multiple Communications Based on EDU-CIAA

The argentinian CIAA project carries out the development and production of embedded systems, for educational, industrial and scientific purposes. The present work consists in designing a comprehensive embedded system based on the EDU-CIAA NXP version, for educational and applied research purposes. The hardware and software are developed in order to provide a shield of Internet of Things (IoT), providing multiple technologies such as: wifi, bluetooth, GSM, SD portability, and RF. A practical application is developed for sensing and monitoring multiple agricultural variables remotely. The development has two main parts. The first is based on an ATMEL microcontroller and it consists of the use of sensors nodes and communication modules. In the second part, a central node with processing capability is designed. For specifications, the national development platform EDU-CIAA NXP and the designed IoT expansion board are used in the second stage. The development has an HMI interface for Smartphones and PCs, using an Android App and web server. All software and hardware developed are shared in an open repository on GitHub.Fil: Juan, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Wearable Full-Body Inertial Measurement with Task Classification Using Deep Learning

In this work, an embedded system is developed for the non-invasive sensing and storage of biomechanical variables of people. It takes advantage of wearable technology, distributing sensors in strategic points of the body, ergonomically and functionally. The results are verified by recording and analysing tasks performed by six subjects to form a database. These tasks include being stood up, sitting down or standing up from a chair, going upstairs and downstairs and walking. Additionally, a convolutional neural network is tested for offline task classification. This work aims to initiate a process that ends in assistance-oriented applications, for the development of better injury rehabilitation techniques and support for elder people, among others. In this way, it seeks to open a path towards an improvement in the living conditions of people with and without reduced activities of daily living capacity.Fil: Gaia Amorós, Jeremías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Bispectrum-based features classification for myoelectric control

Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.Fil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Deep learning-based classification using Cumulants and Bispectrum of EMG signals

Surface electromyographic signals (EMG) historically have been used to classify tasks in basis of a feature extraction scheme and low complexity classifiers. Deep networks, as Multilayer Perceptron and Convolutional Neural Network (MLP and CNN, respectively), avoid the traditional, complex and heuristic (handcrafted) process of feature extraction. Today, it is possible to face the computational cost that these automatic techniques require due to the technology advancement. This allowed deep learning techniques to be quickly generalized to countless applications. This paper proposes to use the third order cumulants and their 2D Fourier transform (Bispectrum) to directly feed CNN and MLP deep learning networks. The classifier is not user-dependent (same classifier for all users) and obtains better results than the classical scheme according to several metrics.Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Gaia Amorós, Jeremías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Robust human machine interface based on head movements applied to assistive robotics

This paper presents an interface that uses two different sensing techniques and combines both results through a fusion process to obtain the minimum-variance estimator of the orientation of the user’s head. Sensing techniques of the interface are based on an inertial sensor and artificial vision. The orientation of the user’s head is used to steer the navigation of a robotic wheelchair. Also, a control algorithm for assistive technology system is presented. The system is evaluated by four individuals with severe motors disability and a quantitative index was developed, in order to objectively evaluate the performance. The results obtained are promising since most users could perform the proposed tasks with the robotic wheelchair.Fil: Perez, Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mut, Vicente Antonio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Freire Bastos, Teodiano. Universidade Federal do Espírito Santo; Brasi

On the use of high-order cumulant and bispectrum formuscular-activity detection

The electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentin