Anàlisi multi-escala de les causes biomecàniques de la coxartrosi juvenil

Aquest projecte pretén ajudar a la comprensió de la mecànica de l’articulació del maluc, en general, i a la determinació de les causes biomecàniques de la coxartrosi, en particular –l’artrosi del maluc–. L’objectiu principal és calcular, a partir de dades de moviment i força obtingudes al laboratori, la distribució de la pressió hidrostàtica al cartílag articular en un instant del moviment de marxa humana enregistrat al laboratori. El mètode multi–escala que es descriu en aquest text utilitza dos models. El model tridimensional musculoesquelètic de sòlids rígids s’utilitza per reproduir el moviment del laboratori i calcular el valor de les forces musculars i articulars mitjançant una anàlisi de dinàmica inversa. I la simulació amb el model d’elements finits del fèmur, la pelvis i el cartílag articular, permet calcular la pressió al cartílag articular. Les condicions de contorn del model d’elements finits es calculen amb les dades de cinemàtica i dinàmica provinents del model de sòlids rígids. Per tal d’emular el moviment en una simulació estàtica, s’utilitza un força volumètrica sobre el fèmur que correspon a la força d’inèrcia de d’Alembert. Els valor màxim de la pressió hidrostàtica al cartílag per a un instant de la marxa enregistrada al laboratori és de 3,5 MPa, un 6% major que el valor de referència utilitzat (3,3 MPa). Aquest valor s’ha ajustat fent variar els paràmetres del mètode de contacte a la superfície del cartílag del model d’elements finits, així, s’ha obtingut una distribució de la pressió més semblant a la real. Tot el procediment que es descriu aquí, serveix com a precedent per a la simulació de l’articulació del maluc d’un moviment realitat al laboratori. En futurs treballs, la modificació dels models per tal d’adequar-los a determinats pacients, pot ajudar a analitzar la biomecànica del maluc i, en particular, a determinar les causes de la coxartrosi juvenil

Rehabilitation of Musculoskeletal Models Using Deep Reinforcement Learning

Neural rehabilitation is a long and complex process that patients undergo after suffering a nervous system injury, such as stroke. These kinds of injuries usually result in brain cells death and partial loss of mobility and coordination. During rehabilitation, the patient performs a series of movements and physical exercises that promote neural plasticity, the brain’s mechanism to regenerate and make new pathways that substitute the damaged connections. Unfortunately, full recovery is almost impossible. The rehabilitation process is tailored to the patient based on the physician’s expertise, and it evolves with the patient’s needs and recovery. However, few computational models for rehabilitation have been developed. For instance, Lee et al. [1] trained a musculoskeletal model of a healthy subject using deep reinforcement learning, and then a prosthetic leg was added to simulate an injury. Results showed how the artificial neural network that controlled muscle contraction was able to adapt and learn to move with the prosthetic leg. Here we show how deep reinforcement learning can be used to control a musculoskeletal model. The algorithm is able to learn new and stable motions by maximizing the so-called reward function. The nervous system is modelled with an artificial neural network, and the deep deterministic policy gradient (DDPG) algorithm is used to train the model in a simulated environment.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (author's final draft

The discretized coulomb friction model in a non-singular complementarity formulation for multibody systems with contacts

Postprint (published version

Analysis of friction coupling and the Painlevé paradox in multibody systems

Multibody models are useful to describe the macroscopic motion of the elements of physical systems. Modeling contact in such systems can be challenging, especially if friction at the contact interface is taken into account. Furthermore, the dynamics equations of multibody systems with contacts and Coulomb friction may become ill-posed due to friction coupling, as in the Painlevé paradox, where a solution for system dynamics may not be found. Here, the dynamics problem is considered following a general approach so that friction phenomena, such as dynamic jamming, can be analyzed. The effect of the contact forces on the velocity field of the system is the cornerstone of the proposed formulation, which is used to analyze friction coupling in multibody systems with a single contact. In addition, we introduce a new representation of the so-called generalized friction cone, a quadratic form defined in the contact velocity space. The geometry of this cone can be used to determine the critical cases where the solvability of the system dynamic equations can be compromised. Moreover, it allows for assessing friction coupling at the contact interface, and obtaining the values of the friction coefficient that can make the dynamics formulation inconsistent. Finally, the classical Painlevé example of a single rod and the multibody model of an articulated arm are used to illustrate how the proposed cone can detect the cases where the dynamic equations have no solution, or multiple solutions.Postprint (author's final draft

Multibody system dynamics interface modelling for stable multirate co-simulation of multiphysics systems

[Abstract] Many industrial applications benefit from predictive computer simulation to reduce costs and time, and shorten product development cycle. Computational multibody system dynamics formalisms and software tools have proved to be particularly useful in the simulation of machinery and mechanical systems. Nowadays, however, the complexity of the applications under study often makes it necessary to consider the interaction of mechanical systems with other components of different nature, physical behaviour, and time scale, such as hydraulics or electronics. Co-simulation is an increasingly important approach to formulate and solve the dynamics of these multiphysics setups. In these, modelling techniques and solvers that are tailored to the requirements of each subsystem execute in parallel and are coupled via the exchange of a limited number of inputs and outputs at certain communication times. Co-simulation has clear potential in the modelling of complex engineering systems. On the other hand, there are also challenges. The use of co-simulation may compromise the stability of the numerical solution, especially when non-iterative coupling schemes are used. In this work, we introduce a modelling technique to improve the dynamic interfacing of mechanical systems in co-simulation setups, based on a reduced representation of multibody systems. This reduced order model is used to obtain a physically meaningful prediction of the evolution of the multibody subsystem dynamics that enables the improvement of the solution of other subsystems. The technique is illustrated in the co-simulation of some examples that include both mechanical and hydraulic components. Results show that dynamic interfaces based on reduced models can be used to improve the stability of non-iterative co-simulation schemes in multiphysics engineering systems, enabling the use of larger communication stepsizes.Natural Sciences and Engineering Research Council Canada (NSERC)CMLabs Simulations, In

Analysis and design of a passive assistive device for patients with Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is the most common muscular dystrophy diagnosed during childhood and is characterized by progressive muscle weakness and loss of muscle mass. DMD is a genetic disease that causes alterations in the dystrophin protein, which protects muscle fibers. Without dystrophin, muscles are broken down by enzymes, which leads to the death of muscle cells and tissue, and causes degeneration and muscle weakness. Because DMD is inherited in an X-linked recessive pattern, it manifests mainly in males (1 in 5000). The mutation of the gene is usually transmitted from mother to child, but it may also occur by spontaneous mutationsPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (author's final draft

Model-based coupling for co-simulation of robotic contact tasks

[Abstract] Co-simulation of complex robotic systems allows the different components to be modelled and simulated independently using methods and tools tailored to their nature and time-scale, which makes the implementation process more modular and flexible. Some applications require the use of non-iterative coupling schemes for optimal performance, such as real-time interactive environments and human and hardware-in-the-loop setups. Stability of non-iterative schemes is challenging due to the restricted and delayed information that is exchanged between subsystems, and robust prediction of interface variables is key. Here, we propose a framework for exchanging model information between mechanical systems with contact, where reduced-order models approximate the interface dynamics of the subsystems. Effective mass and force terms are formulated using a reduced representation of the model, which can then be exchanged between subsystems and integrated in their simulation. The analysis of several simulations of challenging robotic contact tasks, such as grasping and insertion with jamming, shows that model-based coupling allows for stable co-simulation with larger interface stiffness values, resulting in stronger coupling and higher simulation accuracy.MINECO; RYC-2016-2022

Assisted Walking with Hybrid Orthosis Using Functional Electrical Stimulation

Neurological disorders affect body mobility, strength and coordination, and can significantly impact the quality of life. For instance, suffering a spinal cord injury (SCI) generally result in permanent lower-limbs paralysis. Robotic assistive devices, such as lower-limb exoskeletons, can help SCI patients to recover their mobility and autonomy in everyday life. Moreover, advances in neuroprosthetics have shown that functional electrical stimulation (FES) can be used to control joint motion by inducing muscle contraction through electrical assistance [1]. Hybrid assistive devices that combine wearable robotics and neuroprosthesis present some advantages compared to robotic assistive devices. Namely, FES-induced muscle contraction activates muscle metabolism, which delays muscle atrophy and promotes cardiovascular activity. However, determining the optimal control strategy of hybrid devices is still a challenge. Here, we present an optimization framework for musculoskeletal models with hybrid assistive devices using the direct collocation methodPeer ReviewedPostprint (author's final draft

Modelling, optimization, and simulation of a robotic assistive device for walking

Human movement disorders such as stroke can result in severe long-term motor disability affecting daily life. Walking difficulty is a key aspect of motor disorders and the most important deficit patients want to overcome. Rehabilitation technology has achieved great clinical efficacy for motor impaired patients. However, limited customization, low acceptability, technical complexity, and high cost are unresolved. Thus, further investigation is necessary. In this work, we propose a model-based framework to study the interaction dynamics between the user and a gait assistance robotic device. Model parameters were estimated from motion data of a healthy subject while wearing the device.Peer ReviewedPostprint (published version

Modelling neuromusculoskeletal response to functional electrical stimulation

A model for functional electrical stimulation with hysteretic muscle recruitment is used to reproduce experimental tibialis anterior stimulation to control ankle dorsiflexion. The subject-specific parameters of the muscle recruitment model where identified from experimental data by solving a nonlinear least-squares problemPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version