Neuromuscular response to whole body vibration treatment during static and dynamic squat exercises

The impact of whole body vibrations (vibration stimulus mechanically transferred to the body) on muscular activity and neuromuscular response has been widely studied but without standard protocol and by using different kinds of exercises and parameters. In this study, we investigated how whole body vibration treatments affect electromyographic signal of rectus femoris during static and dynamic squat exercises. The aim was the identification of squat exercise characteristics useful to maximize neuromuscular activation and hence progress in training efficacy. Fourteen healthy volunteers performed both static and dynamic squat exercises without and with vibration treatments. Surface electromyographic signals of rectus femoris were recorded during the whole exercise and processed to reduce artifacts and to extract root mean square values. Paired t-test results demonstrated an increase of the root mean square values (p<0.05) in both static and dynamic squat exercises with vibrations respectively of 63% and 108%. For each exercise, subjects gave a rating of the perceived exertion according to the Borg's scale but there were no significant changes in the perceived exertion rate between exercises with and without vibration. Finally, results from analysis of electromyographic signals identified the static squat with WBV treatment as the exercise with higher neuromuscular system response. © 2012 IEEE.

Relevance of motion artifact in electromyography recordings during vibration treatment

Electromyography readings (EMGs) from quadriceps of fifteen subjects were recorded during whole body vibration treatment at different frequencies (10-50 Hz). Additional electrodes were placed on the patella to monitor the occurrence of motion artifact, triaxial accelerometers were placed onto quadriceps to monitor motion. Signal spectra revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with the accelerometer data. EMG total power was compared to that associated with vibration harmonics narrow bands, before and during vibration. On average, vibration associated power resulted in only 3% (±0.9%) of the total power prior to vibration and 29% (±13.4%) during vibration. Often, studies employ surface EMG to quantitatively evaluate vibration evoked muscular activity and to set stimulation frequency. However, previous research has not accounted for motion artifacts. The data presented in this study emphasize the need for the removal of motion artifacts, as they consistently affect RMS estimation, which is often used as a concise muscle activity index during vibrations. Such artifacts, rather unpredictable in amplitude, might be the cause of large inter-study differences and must be eliminated before analysis. Motion artifact filtering will contribute to thorough and precise interpretation of neuromuscular response to vibration treatment. © 2008 Elsevier Ltd. All rights reserved.

Whole body vibration training:analysis and characterization

The aim of this work is to contribute to the analysis and characterization of training with whole body vibration (WBV) and the resultant neuromuscular response. WBV aims to mechanically activate muscle by eliciting stretch reflexes. Generally, surface electromyography is utilized to assess muscular response elicited by vibrations. However, EMG analysis could potentially bring to erroneous conclusions if not accurately filtered. Tiny and lightweight MEMS accelerometers were found helpful in monitoring muscle motion. Displacements were estimated integrating twice the acceleration data after gravity and small postural subject adjustments contribution removal. Results showed the relevant presence of motion artifacts on EMG recordings, the high correlation between muscle motion and EMG activity and how resonance frequencies and dumping factors depended on subject and his positioning onto the vibrating platform. Stimulations at the resonant frequency maximize muscles lengthening and in turn, muscle spindle solicitation , which may produce more muscle activation. Local mechanical stimulus characterization (Le, muscle motion analysis) could be meaningful in discovering proper muscle stimulation and may contribute to suggest appropriate and effective WBV exercise protocols. ©2009 IEEE.

Muscle movement and electrodes motion artifact during vibration treatment

Vibration treatment by oscillating platforms is more and more employed in the fields of exercise physiology and bone research. The rationale of this treatment is based on the neuromuscular system response elicited by vibration loads. surface Electromyography (EMG) is largely utilized to assess muscular response elicited by vibrations and Root Mean Square of the electromyography signals is often used as a concise quantitative index of muscle activity; in general, EMG envelope or RMS is expected to increase during vibration. However, it is well known that during surface bio-potential recording, motion artifacts may arise from relative motion between electrodes and skin and between skin layers. Also the only skin stretch, modifying the internal charge distribution, results in a variation of electrode potential. The aim of this study is to highlight the movements of muscles, and the succeeding relevance of motion artifacts on electrodes, in subjects undergoing vibration treatments. EMGs from quadriceps of fifteen subjects were recorded during vibration at different frequencies (15-40 Hz); Triaxial accelerometers were placed onto quadriceps, as close as possible to muscle belly, to monitor motion. The computed muscle belly displacements showed a peculiar behavior reflecting the mechanical properties of the structures involved. Motion artifact related to the impressed vibration have been recognized and related to movement of the soft tissues. In fact large artifacts are visible on EMGs and patellar electrodes recordings during vibration. Signals spectra also revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with accelerometers data. © 2008 Springer-Verlag.

Signal processing and information fusing algorithms for the synthesis of an alternative electromyogram/eye gaze tracking computer cursor control system

This research pursued the conceptualization and real-time verification of a system that allows a computer user to control the cursor of a computer interface without using his/her hands. The target user groups for this system are individuals who are unable to use their hands due to spinal dysfunction or other afflictions, and individuals who must use their hands for higher priority tasks while still requiring interaction with a computer. ^ The system receives two forms of input from the user: Electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an Eye Gaze Tracking (EGT) system. In order to produce reliable cursor control from the two forms of user input, the development of this EMG/EGT system addressed three key requirements: an algorithm was created to accurately translate EMG signals due to facial movements into cursor actions, a separate algorithm was created that recognized an eye gaze fixation and provided an estimate of the associated eye gaze position, and an information fusion protocol was devised to efficiently integrate the outputs of these algorithms. ^ Experiments were conducted to compare the performance of EMG/EGT cursor control to EGT-only control and mouse control. These experiments took the form of two different types of point-and-click trials. The data produced by these experiments were evaluated using statistical analysis, Fitts' Law analysis and target re-entry (TRE) analysis. ^ The experimental results revealed that though EMG/EGT control was slower than EGT-only and mouse control, it provided effective hands-free control of the cursor without a spatial accuracy limitation, and it also facilitated a reliable click operation. This combination of qualities is not possessed by either EGT-only or mouse control, making EMG/EGT cursor control a unique and practical alternative for a user's cursor control needs. ^

Efeito tardio do kinesio taping na função do membro inferior e no desempenho neuromuscular do quadríceps femoral de mulheres saudáveis: ensaio clínico e randomizado

Introduction: Kinesio Taping (KT) has been used in healthy people to improve neuromuscular performance, however, few studies have evaluated its chronic effects, despite being suggested. Objective: To analyze the chronic effects of KT on neuromuscular performance of the quadriceps, the oscillation of the center of pressure and lower limb function in healthy women. Methods: blinded, randomized, controlled trial, composed of 60 women (mean age 21.9 ± 3.3 years and BMI 22.3 ± 2.2 kg / m2) submitted to the evaluation of oscillation of the center of pressure through the baropodometry, the lower limb function by the hop test, isokinetic knee performance, the electromyographic activity of the vastus lateralis (VL) and joint position sense of the knee (JPS). Then, participants were randomly divided into three groups of twenty: control - did not apply the KT; placebo - application of KT without tension on the quadriceps; Kinesio Taping - application of KT with tension in the same muscle group. The evaluations were conducted in five moments: prior to application of KT, immediately after the application, 24h, 48h after application and 24 hours after its removal (72h). SPSS 20.0 was used for statistical analysis. The KS test was used to verify the data normality, the Levene test for homogeneity of variances and a mixed-model ANOVA 3x5 to check intra and inter-group differences. Results: there was no difference in peak torque, the power, nor the electromyographic activity or SPA (p> 0.05) between groups. The displacement speed of center of pressure reduced immediately after the application on kinesio taping group (p <0.001), but with no differences between the groups (p = 0.28). There was a reduction in the time of peak torque among the three groups in the evaluations after KT application (p <0.001) and an increase in single hop in all groups (p <0.001), but with no differences between them. Conclusion: KT can not change, in a chronic way, the lower limb function, the oscillation of the center of pressure, the isokinetic performance, the JPS of the knee and the electromyographic activity of VL muscle in healthy women.

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Application of a new method in the study of pelvic floor muscle passive properties in continent women

The aim of this study was to present a new methodology for evaluating the pelvic floor muscle (PFM) passive properties. The properties were assessed in 13 continent women using an intra-vaginal dynamometric speculum and EMG (to ensure the subjects were relaxed) in four different conditions: (1) forces recorded at minimal aperture (initial passive resistance); (2) passive resistance at maximal aperture; (3) forces and passive elastic stiffness (PES) evaluated during five lengthening and shortening cycles; and (4) percentage loss of resistance after 1 min of sustained stretch. The PFMs and surrounding tissues were stretched, at constant speed, by increasing the vaginal antero-posterior diameter; different apertures were considered. Hysteresis was also calculated. The procedure was deemed acceptable by all participants. The median passive forces recorded ranged from 0.54 N (interquartile range 1.52) for minimal aperture to 8.45 N (interquartile range 7.10) for maximal aperture while the corresponding median PES values were 0.17 N/mm (interquartile range 0.28) and 0.67 N/mm (interquartile range 0.60). Median hysteresis was 17.24 N∗mm (interquartile range 35.60) and the median percentage of force losses was 11.17% (interquartile range 13.33). This original approach to evaluating the PFM passive properties is very promising for providing better insight into the patho-physiology of stress urinary incontinence and pinpointing conservative treatment mechanisms.

Évaluation de la force et de l'endurance des muscles du dos chez des sujets sains et lombalgiques : étude comparative de différents tests

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

Évaluation de la force et de l'endurance des muscles para-vertébraux chez des femmes et des hommes : étude comparative de différents tests dynamométriques

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

Rôle du diaphragme au cours de l'expiration chez l'enfant sous ventilation mécanique

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

Étude par pressions, activité musculaire et imagerie du pied pathologique en charge

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

Premotor Mechanisms for Orofacial Coordination

The mouth, throat, and face contain numerous muscles that participate in a large variety of orofacial behaviors. The jaw and tongue can move independently, and thus require a high degree of coordination among the muscles that move them to prevent self-injury. However, different orofacial behaviors require distinct patterns of coordination between these muscles. The method through which motor control circuitry might coordinate this activity has yet to be determined. Electrophysiological, immunohistochemical, and retrograde tracing studies have attempted to identify populations of premotor neurons which directly send information to orofacial motoneurons in an effort to identify sources of coordination. Yet these studies have not provided a complete picture of the population of neurons which monosynaptically connect to jaw and tongue motoneurons. Additionally, while many of these studies have suggested that premotor neurons projecting to multiple motor pools may play a role in coordination of orofacial muscles, no clear functional roles for these neurons in the coordination of natural orofacial movements has been identified.

In this dissertation, I took advantage of the recently developed monosynaptic rabies virus to trace the premotor circuits for the jaw-closing masseter muscle and tongue-protruding genioglossus muscle in the neonatal mouse, uncovering novel premotor inputs in the brainstem. Furthermore, these studies identified a set of neurons which form boutons onto motor neurons in multiple motor pools, providing a premotor substrate for orofacial coordination. I then combined a retrogradely traveling lentivirus with a split-intein mediated split-Cre recombinase system to isolate and manipulate a population of neurons which project to both left and right jaw-closing motor nuclei. I found that these bilaterally projecting neurons also innervate multiple other orofacial motor nuclei, premotor regions, and midbrain regions implicated in motor control. I anatomically and physiologically characterized these neurons and used optogenetic and chemicogenetic approaches to assess their role in natural jaw-closing behavior, specifically with reference to bilateral masseter muscle electromyogram (EMG) activity. These studies identified a population of bilaterally projecting neurons in the supratrigeminal nucleus as essential for maintenance of an appropriate level of masseter activation during natural chewing behavior in the freely moving mouse. Moreover, these studies uncovered two distinct roles of supratrigeminal bilaterally projecting neurons in bilaterally synchronized activation of masseter muscles, and active balancing of bilateral masseter muscle tone against an excitatory input. Together, these studies identify neurons which project to multiple motor nuclei as a mechanism by which the brain coordinates orofacial muscles during natural behavior.