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Este trabalho consiste no desenvolvimento de uma metodologia de identificação de sistemas, capaz de modelar a fadiga muscular durante movimentos isométricos voluntários de indivíduos sem deficiência física, através dos dados de sinais eletromiográficos de superfície (sEMG) e de torque, visto que a literatura já tem referências sobre a utilização desses parâmetros. Foi realizado um protocolo com o membro inferior dominante do voluntário para que a fadiga muscular fosse atingida. A medição do torque foi realizada pelo isocionético Biodex System 4 Proe a coleta da eletromiografia (EMG) foi realizada pelo EMG_800C com eletrodos Ag/AgCl de superfície, não invasivos e na configuração bipolar. O software Labview foi utilizado para a aquisição desses dados e o Matlab® para o processamento dos sinais e identificação do sistemaestudado. O objetivo foi encontrar um modelo que relacione essas duas medidas de fadiga muscular, utilizando para isso a eletromiografia como entrada e o torque como saída do sistema. Nessa análise, a metodologia proposta envolveu o modelo NARX (Nonlinear Auto-Regressive with eXogenous Inputs) juntamente com os algoritmos de taxa de redução de erro (ERR), critérios de Akaike (AIC) e de Bayes (BIC). Os resultados apresentados demonstram que o modelo propostopara cada voluntário pode ser utilizado devida a semelhança estatística encontrada, além disso, todos os modelos sugeridos obtiveram bons resultados quando comparados a outros trabalhos da literatura. O erro RMS médio para o modelo com 15 regressores foi de 5,24% ± 2,59%; já para 10 regressores foi de 6,89% ± 2,83% e; para o modelo geral, 5,54% ± 3,21. Isso permite concluir que a metodologia adotada neste trabalho obteve resultados satisfatórios para as condições estudadas. This work consists of the development of a system identification methodology capable of modeling muscle fatigue during voluntary isometric movements of individuals without physical disability, using surface electromyographic (sEMG) and torque data. A protocol was performed with the dominant lower limb of the volunteer so that muscle fatigue was achieved. The torque measurement was performed by the isokinetic Biodex System 4 Pro and EMG was performed by the EMG_800C with non - invasive surface Ag / AgCl electrodes and in the bipolar configuration. Labview software was used for the acquisition of this data and Matlab® for signal processing and identification of the system studied. The objective was to find a model that relates two measures of muscular fatigue, using for this the electromyography as input and the torque as output of the system. In this analysis, the proposed methodology involved the NARX (Nonlinear Auto-Regressive with eXogenous Inputs) model, with the error reduction rate (ERR), Akaike (AIC) and Bayes (BIC) criteria algorithms. The results show that the model proposed for each volunteer can be used due to the statistical similarity found. In addition, all the suggested models obtained good results when compared to other studies in the literature. The mean RMS error for the model with 15 regressors was 5.24% ± 2.59%; already for 10 regressors was 6.89% ± 2.83% and; for the general model, 5.54% ± 3.21. This allows concluding that the methodology adopted in this workwas successful for the conditions studied.

Objective In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG signal samples collected from the operator. However, this control strategy may be fatiguing. In this study, we proposed and experimentally validated a novel stiffness control strategy, based on the average muscle co-contraction estimated from EMG samples collected in the previous 1 or 2 s. Approach Nine subjects performed a tracking task with their right wrist in five different sessions. In four sessions a haptic device (Hi-5) applied a sinusoidal perturbing torque. In Baseline session, co-contraction reduced the effect of the perturbation only by stiffening the wrist. In contrast, during aided sessions the perturbation amplitude was also reduced (mimicking the effect of additional stiffening provided by EMG-driven robotic device) either proportionally to the co-contraction exerted by the subject sample-by-sample (Proportional), or according to the average co-contraction exerted in the previous 1 s (Integral 1s), or 2 s (Integral 2s). Task error, metabolic cost during the tracking task, perceived fatigue, and the median EMG frequency calculated during a sub-maximal isometric torque generation tasks that alternated with the tracking were compared across sessions. Main results Positive effects of the reduction of the perturbation provided by co-contraction estimation was identified in all the investigated variables. Integral 1s session showed lower metabolic cost with respect to the Proportional session, and lower perceived fatigue with respect to both the Proportional and the Integral 2s sessions. Significance This study's results showed that controlling the stiffness of an EMG-driven robotic device proportionally to the operator's co-contraction, averaged in the previous 1 s, represents the best control strategy because it required less metabolic cost and led to a lower perceived fatigue.

Submitted by Christianne Fontes de Andrade (cfontes@ct.ufrj.br) on 2019-10-16T17:03:06Z No. of bitstreams: 1 876906.pdf: 1342452 bytes, checksum: 7ebe8469bf9a8e6b621eb33fa4da889b (MD5) Made available in DSpace on 2019-10-16T17:03:06Z (GMT). No. of bitstreams: 1 876906.pdf: 1342452 bytes, checksum: 7ebe8469bf9a8e6b621eb33fa4da889b (MD5) Previous issue date: 2017-03 A Doença de Parkinson (DP) é uma doença neurodegenerativa progressiva caracterizada pelo tremor de repouso, bradicinesia, rigidez e a instabilidade postural. Leva a alterações músculo-esqueléticas, como fraqueza e encurtamento muscular, o que interfere diretamente na função motora. O objetivo deste estudo foi quantificar a distribuição do torque isométrico do tornozelo entre os componentes do músculo tríceps sural (TS): gastrocnêmios medial (GM), lateral (GL), sóleo, bem como a coativação do tibial anterior (TA). Dois grupos, idosos com DP e idosos aparentemente hígidos, foram instruídos a realizar degraus de flexão plantar isométrica a 10, 40 e 60% do torque máximo (CVM) em um dinamômetro isocinético. Sinais de torque e de eletromiografia (EMG) foram adquiridos sincronicamente. Um modelo da mecânica muscular acionada por EMG (EMG-Driven) foi aplicado para estimar o torque gerado por cada um dos quatro músculos. O erro médio quadrático entre o torque total estimado e o medido foi calculado. No grupo DP, o compartilhamento de torque seguiu uma ordem crescente de contribuições, com relação aos componentes do tríceps sural para o torque total de flexão plantar: degrau 10%: GM=17,9%, GL=19,7%, Sóleo=62,4%; degrau 40%: GM=21%, GL20,6%, Sóleo=58,4% e degrau 60% GL=18%, GM=24,6%, Sóleo=56,2%. O TA apresentou participação decrescente com o aumento de intensidade (7,8%; 4,7% e 3,9% para os degraus de 10, 40 e 60%, respectivamente). Foi observada uma diferença entre as contribuições do GL (DP>GC) e do GM (DP CG) and GM (PD

New methods able to assess the individual ability of patients to generate motion and adaptation strategies are increasingly required for clinical applications aiming at recovering motor functions. Indeed, more effective rehabilitation treatments are designed to be personalized on the subject capabilities. In this context, neuromusculoskeletal (NMS) models represent a valuable tool, as they can provide important information about the unique anatomical, neurological, and functional characteristics of different subjects, through the computation of human internal variables, such as muscle activations, muscle forces, joint contact forces and moments. A first possible approach is to estimate these values using optimization-based NMS models. However, these models require to make assumptions on how the muscles contribute to the observed movement. More promising are instead NMS models driven by electromyographic signals (EMG), which use experimentally recorded signals that can be considered a direct representation of the subject motor intentions. This allows to account for the actual differences in an individual neuromuscular control system, without making any preliminary assumptions. Therefore these models have the potentialities to provide the level of personalization that is essential for applications in the clinical field. Although EMG-driven NMS models have been investigated in the literature, even for clinical purposes, they are mostly limited to one degree of freedom (DOF), and consider only the muscles spanning that DOF. Additionally, despite the promising results, they are still not introduced in the clinical practice; the main reason possibly being their complexity, that makes them not usable in clinical context, where standard and reliable procedures are required. The importance of EMG-driven NMS modeling for clinical applications would be even higher with the availability of multi-DOF models, as impairments usually compromise multiple joints. Nevertheless, even if a first multi-DOF EMG-driven NMS model for the lower limbs has been recently introduced in literature, its even greater complexity makes more difficult an analysis of its applicability in the clinical field. This work represents a first effort towards a critical analysis of multi-DOF EMG-driven NMS models to evaluate their possible use in clinical practice. To achieve this objective, several issues and limitations have been addressed. In the specific, the attention has been focused on two aspects: (i) making the methodology usable, to foster its adoption by multiple laboratories and research groups, and to facilitate sensitivity analyses required to assess its accuracy; (ii) highlighting the effects of some methodological aspects related to data acquisition and processing, and evaluating their impact on the accuracy of estimated parameters and muscle forces. This analysis is even more important for multi-DOF EMG-driven NMS model as it is still not present in the literature. To accomplish the first goal, a software tool (MOtoNMS) has been developed and it is freely available for the research community. It is a complete, flexible, and user-friendly tool that allows to automatically process experimental motion data from different laboratories in a transparent and repeatable way, for their subsequent use with neuromusculoskeletal modeling software. MOtoNMS generalizes data processing methods across laboratories, and simplifies and speeds up the demanding data elaboration workflow. This simplification represents an indispensable step towards an actual translation of NMS methods in clinical practice. The second part of the work has been, instead, dedicated to analyze the impact on model parameters and muscle forces prediction of different techniques for EMG data collection and processing that are feasible for clinical settings, in particular concentrating on EMGs normalization. Indeed, moving EMG-driven NMS modeling towards clinical applications that deal with multiple DOFs requires to carefully consider subject's motor limitations due to his/her mobility impairments. This results in a rethinking about the methodologies for data acquisition and processing. Therefore, the impact of using only data from walking trials on both calibration of model parameters and computing the maximum EMG values needed for the normalization step, has been assessed with two case studies. Moreover, a protocol for the collection of maximum voluntary contractions has been proposed. This protocol is suitable for multiple DOFs applications involving patients with reduced motor ability and it requires only low-cost and easy to acquire tools to make it applicable in any laboratory. The research proposed in this thesis provides tools to simplify the use of multi-DOF EMG-driven neuromusculoskeletal models and proposes analyses and procedures to evaluate the accuracy and reliability of the obtained results with the aim of pursuing clinical applications.

The prediction of musculo-tendon forces developed during daily living tasks is essential to assess movement control and joint contact forces, and then provide insight to improve diagnosis and treatment follow-up of neurological and orthopedic disorders. Direct measurement of the musculo-tendon forces is hardly possible and the redundancy inherent in the musculo-skeletal system yields not enough equilibrium equations to compute these forces. Different methods have been proposed to overcome this problem, requiring numerous input parameters, most of them difficult or impossible to adjust to a specific subject. These methods will be exposed and their limits pointed out. Anyway, further development is needed in order that the model-based prediction of musculo-tendon forces can be used for clinical purposes.; La prédiction des forces musculo-tendineuses développées au cours des tâches de la vie courante est essentielle pour accéder au contrôle du mouvement et aux actions de contact articulaires, et permet d'améliorer le diagnostic et le suivi du traitement des désordres neurologiques et orthopédiques. La mesure directe des forces musculo-tendineuses est difficilement réalisable et la redondance inhérente au système musculo-squelettique induit un nombre d'équations d'équilibre insuffisant pour calculer ces forces. Plusieurs méthodes ont été proposées pour résoudre ce problème, mais elles requièrent de nombreux paramètre d'entrée, pour la plupart difficiles ou impossibles à ajuster à un sujet spécifique. Ces méthodes vont être décrites et leurs limites soulignées. Quoi qu'il en soit, de nouveaux développements sont nécessaires avant que les forces musculo-tendineuses prédites par les modèles puissent être utilisées dans le cadre d'applications cliniques.

Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction. peerReviewed