Musculoskeletal Models in a Clinical Perspective

This book includes a selection of papers showing the potential of the dynamic modelling approach to treat problems related to the musculoskeletal system. The state-of-the-art is presented in a review article and in a perspective paper, and several examples of application in different clinical problems are provided

Otimização muscle-in-the-loop em tempo real para reabilitação física com um exosqueleto ativo: uma mudança de paradigma

Assisting human locomotion with a wearable robotic orthosis is still quite challenging, largely due to the complexity of the neuromusculoskeletal system, the time-varying dynamics that accompany motor adaptation, and the uniqueness of every individual’s response to the assistance given by the robot. To this day, these devices have not met their well-known promise yet, mostly due to the fact that they are not perfectly suitable for the rehabilitation of neuropathologic patients. One of the main challenges hampering this goal still relies on the interface and co-dependency between the human and the machine. Nowadays, most commercial exoskeletons replay pre-defined gait patterns, whereas research exoskeletons are switching to controllers based on optimized torque profiles. In most cases, the dynamics of the human musculoskeletal system are still ignored and do not take into account the optimal conditions for inducing a positive modulation of neuromuscular activity. This is because both rehabilitation strategies are still emphasized on the macro level of the whole joint instead of focusing on the muscles’ dynamics and activity, which are the actual anatomical elements that may need to be rehabilitated. Strategies to keep the human in the loop of the exoskeleton’s control laws in real-time may help to overcome these challenges. The main purpose of the present dissertation is to make a paradigm shift in the approach on how the assistance that is given to a subject by an exoskeleton is modelled and controlled during physical rehabilitation. Therefore, in the scope of the present work, it was intended to design, concede, implement, and validate a real-time muscle-in-the-loop optimization model to find the best assistive support ratio that would induce optimal rehabilitation conditions to a specific group of impaired muscles while having a minimum impact on the other healthy muscles. The developed optimization model was implemented in the form of a plugin and was integrated on a neuromechanical model-based interface for driving a bilateral ankle exoskeleton. Experimental pilot tests evaluated the feasibility and effectiveness of the model. Results of the most significant pilots achieved EMG reductions up to 61 ± 3 % in Soleus and 41 ± 10 % in Gastrocnemius Lateralis. Moreover, results also demonstrated the efficiency of the optimization’s specific reduction on rehabilitation by looking into the muscular fatigue after each experiment. Finally, two parallel preliminary studies emerged from the pilots, which looked at muscle adaptation, after a new assistive condition had been applied, over time and at the effect of the lateral positioning of the exoskeleton’s actuators on the leg muscles.Auxiliar a locomoção humana com uma ortose robótica ainda é bastante desafiante, em grande parte devido à complexidade do sistema neuromusculoesquelético, à dinâmica variável no tempo que acompanha a adaptação motora e à singularidade da resposta de cada indivíduo à assistência dada pelo robô. Até hoje, está por cumprir a promessa inicial destes dispositivos, principalmente devido ao facto de não serem perfeitamente adequados para a reabilitação de pacientes neuropatológicos. Um dos principais desafios que dificultam esse objetivo foca-se ainda na interface e na co-dependência entre o ser humano e a máquina. Hoje em dia, a maioria dos exoesqueletos comerciais reproduz padrões de marcha predefinidos, enquanto que os exoesqueletos em investigação estão só agora a mudar para controladores com base em perfis de binário otimizados. Na maioria dos casos, a dinâmica do sistema musculoesquelético humano ainda é ignorada e não tem em consideração as condições ideais para induzir uma modulação positiva da atividade neuromuscular. Isso ocorre porque ambas as estratégias de reabilitação ainda são enfatizadas no nível macro de toda a articulação, em vez de se concentrar na dinâmica e atividade dos músculos, que são os elementos anatómicos que realmente precisam de ser reabilitados. Estratégias para manter o ser humano em loop nos comandos que controlam o exoesqueleto em tempo real podem ajudar a superar estes desafios. O principal objetivo desta dissertação é fazer uma mudança de paradigma na abordagem em como a assistência que é dada a um sujeito por um exosqueleto é modelada e controlada durante a reabilitação física. Portanto, no contexto do presente trabalho, pretendeu-se projetar, conceder, implementar e validar um modelo de otimização muscle-in-the-loop em tempo real para encontrar a melhor relação de suporte capaz de induzir as condições ideais de reabilitação para um grupo específico de músculos fragilizados, tendo um impacto mínimo nos outros músculos saudáveis. O modelo de otimização desenvolvido foi implementado na forma de um plugin e foi integrado numa interface baseada num modelo neuromecânico para o controlo de um exoesqueleto bilateral de tornozelo. Testes experimentais piloto avaliaram a viabilidade e a eficácia do modelo. Os resultados dos testes mais significativos demonstraram reduções de EMG de até 61 ± 3 % no Soleus e 41 ± 10 % no Gastrocnemius Lateral. Adicionalmente, os resultados demonstraram também a eficiência em reabilitação da redução específica no EMG devido à otimização tendo em conta a fadiga muscular após cada teste. Finalmente, dois estudos preliminares paralelos emergiram dos testes piloto, que analisaram a adaptação muscular após uma nova condição assistiva ter sido definida ao longo do tempo e o efeito do posicionamento lateral dos atuadores do exoesqueleto nos músculos da perna.Mestrado em Engenharia Biomédic

Model-based myoelectric control of robots for assistance and rehabilitation

The first anthropomorphic robots and exoskeletons were developed with the idea of combining man and machine into an intimate symbiotic unit that can perform as one joint system. A human-robot interface consists of processes of two different nature: (1) the physical interaction (pHRI) between the device and its user and (2) the exchange of cognitive information (cHRI) between the human and the robot. To achieve the symbiosis between the two actors, both need to be optimized. The evolution of mechanical design and the introduction of new materials pushed pHRI to new frontiers on ergonomics and assistance performance. However, cHRI still lacks on this direction because is more complicated: it requires communication from the cognitive processes occuring in the human agent to the robot, e.g. intention detection; but also from the robot to the human agent, e.g. feedback modalities such as haptic cues. A possible innovation is the inclusion of the electromyographic signal, the command signal from our brain to the musculoskeletal system for the movement, in the robot control loop. The aim of this thesis was to develop a real-time control framework for an assistive device that can generate the same force produced by the muscles. To do this, I incorporated in the robot control loop a detailed musculoskeletal model that estimates the net torque at the joint level by taking as inputs the electromyography signals and kinematic data. This module is called myoprocessor. Here I present two applications of this control approach: the first was implemented on a soft wearable arm exosuit in order to evaluate the adaptation of the controller on different motion and loads. The second one, was a generation of myoprocessor-driven force field on a planar robot manipulandum in order to study the modularity changes of the musculoskeletal system. Both applications showed that the device controlled by myoprocessor works symbiotically with the user, by reducing the muscular activity and preserving the motor performance. The ability of seamlessly combining musculoskeletal force estimators with assistive devices opens new avenues for assisting human movement both in healthy and impaired individuals

Machine learning-based dexterous control of hand prostheses

Upper-limb myoelectric prostheses are controlled by muscle activity information recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control can be achieved by deploying statistical and machine learning (ML) tools to decipher the user’s movement intent from EMG signals. This thesis proposes various means of advancing the capabilities of non-invasive, ML-based control of myoelectric hand prostheses. Two main directions are explored, namely classification-based hand grip selection and proportional finger position control using regression methods. Several practical aspects are considered with the aim of maximising the clinical impact of the proposed methodologies, which are evaluated with offline analyses as well as real-time experiments involving both able-bodied and transradial amputee participants. It has been generally accepted that the EMG signal may not always be a reliable source of control information for prostheses, mainly due to its stochastic and non-stationary properties. One particular issue associated with the use of surface EMG signals for upper-extremity myoelectric control is the limb position effect, which is related to the lack of decoding generalisation under novel arm postures. To address this challenge, it is proposed to make concurrent use of EMG sensors and inertial measurement units (IMUs). It is demonstrated this can lead to a significant improvement in both classification accuracy (CA) and real-time prosthetic control performance. Additionally, the relationship between surface EMG and inertial measurements is investigated and it is found that these modalities are partially related due to reflecting different manifestations of the same underlying phenomenon, that is, the muscular activity. In the field of upper-limb myoelectric control, the linear discriminant analysis (LDA) classifier has arguably been the most popular choice for movement intent decoding. This is mainly attributable to its ease of implementation, low computational requirements, and acceptable decoding performance. Nevertheless, this particular method makes a strong fundamental assumption, that is, data observations from different classes share a common covariance structure. Although this assumption may often be violated in practice, it has been found that the performance of the method is comparable to that of more sophisticated algorithms. In this thesis, it is proposed to remove this assumption by making use of general class-conditional Gaussian models and appropriate regularisation to avoid overfitting issues. By performing an exhaustive analysis on benchmark datasets, it is demonstrated that the proposed approach based on regularised discriminant analysis (RDA) can offer an impressive increase in decoding accuracy. By combining the use of RDA classification with a novel confidence-based rejection policy that intends to minimise the rate of unintended hand motions, it is shown that it is feasible to attain robust myoelectric grip control of a prosthetic hand by making use of a single pair of surface EMG-IMU sensors. Most present-day commercial prosthetic hands offer the mechanical abilities to support individual digit control; however, classification-based methods can only produce pre-defined grip patterns, a feature which results in prosthesis under-actuation. Although classification-based grip control can provide a great advantage over conventional strategies, it is far from being intuitive and natural to the user. A potential way of approaching the level of dexterity enjoyed by the human hand is via continuous and individual control of multiple joints. To this end, an exhaustive analysis is performed on the feasibility of reconstructing multidimensional hand joint angles from surface EMG signals. A supervised method based on the eigenvalue formulation of multiple linear regression (MLR) is then proposed to simultaneously reduce the dimensionality of input and output variables and its performance is compared to that of typically used unsupervised methods, which may produce suboptimal results in this context. An experimental paradigm is finally designed to evaluate the efficacy of the proposed finger position control scheme during real-time prosthesis use. This thesis provides insight into the capacity of deploying a range of computational methods for non-invasive myoelectric control. It contributes towards developing intuitive interfaces for dexterous control of multi-articulated prosthetic hands by transradial amputees

Proceedings XXII Congresso SIAMOC 2022

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica dà l’occasione a tutti i professionisti, dell’ambito clinico e ingegneristico, di incontrarsi, presentare le proprie ricerche e rimanere aggiornati sulle più recenti innovazioni nell’ambito dell’applicazione clinica dei metodi di analisi del movimento, al fine di promuoverne lo studio e le applicazioni cliniche per migliorare la valutazione dei disordini motori, aumentare l’efficacia dei trattamenti attraverso l’analisi quantitativa dei dati e una più focalizzata pianificazione dei trattamenti, ed inoltre per quantificare i risultati delle terapie correnti