A practical system for hands-off computer interaction based on electromyogram (EMG)/eye gaze tracking (EGT)

Effective interaction with personal computers is a basic requirement for many of the functions that are performed in our daily lives. With the rapid emergence of the Internet and the World Wide Web, computers have become one of the premier means of communication in our society. Unfortunately, these advances have not become equally accessible to physically handicapped individuals. In reality, a significant number of individuals with severe motor disabilities, due to a variety of causes such as Spinal Cord Injury (SCI), Amyothrophic Lateral Sclerosis (ALS), etc., may not be able to utilize the computer mouse as a vital input device for computer interaction. The purpose of this research was to further develop and improve an existing alternative input device for computer cursor control to be used by individuals with severe motor disabilities. This thesis describes the development and the underlying principle for a practical hands-off human-computer interface based on Electromyogram (EMG) signals and Eye Gaze Tracking (EGT) technology compatible with the Microsoft Windows operating system (OS). Results of the software developed in this thesis show a significant improvement in the performance and usability of the EMG/EGT cursor control HCI.

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

Activité EMG des muscles du dos chez des patients dystrophiques

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Activité EMG au niveau du membre supérieur : étude à l'aide d'un fantôme

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Décomposition automatique de signaux EMG intramusculaires

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Activité EMG des muscles du dos chez des patients dystrophiques

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Activité EMG au niveau du membre supérieur : étude à l'aide d'un fantôme

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Décomposition automatique de signaux EMG intramusculaires

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Responsiveness of the frontal EMG for monitoring the sedation state of critically ill patients.

Background. Excessive sedation is associated with adverse patient outcomes during critical illness, and a validated monitoring technology could improve care. We developed a novel method, the responsiveness index (RI) of the frontal EMG. We compared RI data with Ramsay clinical sedation assessments in general and cardiac intensive care unit (ICU) patients. Methods. We developed the algorithm by iterative analysis of detailed observational data in 30 medical–surgical ICU patients and described its performance in this cohort and 15 patients recovering from scheduled cardiac surgery. Continuous EMG data were collected via frontal electrodes and RI data compared with modified Ramsay sedation state assessments recorded regularly by a blinded trained observer. RI performance was compared with EntropyTM across Ramsay categories to assess validity. Results. RI correlated well with the Ramsay category, especially for the cardiac surgery cohort (general ICU patients r¼0.55; cardiac surgery patients r¼0.85, both P,0.0001). Discrimination across all Ramsay categories was reasonable in the general ICU patient cohort [PK¼0.74 (SEM 0.02)] and excellent in the cardiac surgery cohort [PK¼0.92 (0.02)]. Discrimination between ‘lighter’ vs ‘deeper’ (Ramsay 1–3 vs 4–6) was good for general ICU patients [PK¼0.80 (0.02)] and excellent for cardiac surgery patients [PK¼0.96 (0.02)]. Performance was significantly better than EntropyTM. Examination of individual cases suggested good face validity. Conclusions. RI of the frontal EMG has promise as a continuous sedation state monitor in critically ill patients. Further investigation to determine its utility in ICU decision-making is warranted.

Pattern recognition methods for EMG prosthetic control

In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an automatic feature selection procedure as a minimization problem in order to improve the method and its robustness.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.

Efeito neuromuscular das técnicas fisioterapeuticas bandagem funcional e corrente interferencial na síndrome tensional cervical

Universidade Estadual de Campinas . Faculdade de Educação Física

Influencia de diferentes informações quanto ao tempo de realização do exercicio sobre a atividade eletromiografica e consumo de oxigenio em ciclistas

Universidade Estadual de Campinas . Faculdade de Educação Física

Torque articular e ativação dos musculos biceps femoral e semi-tendineo durante movimentos isocineticos de flexão do joelho em atletas de futebol

Universidade Estadual de Campinas . Faculdade de Educação Física