1,721,090 research outputs found
A Neuromuscular Human-Machine Interface for Applications in Rehabilitation Robotics
No full textThis research work presents a novel neuromusculoskeletal (NMS) model of the human lower limb that is physiologically accurate and computationally fast. The NMS model uses electromyography (EMG) signals recorded from 16 muscles to
predict the force developed by 34 musculotendon actuators (MTAs). The operation of each MTA is constrained to simultaneously satisfy the joint moments generated with respect to 4 degrees of freedom (DOF) including: hip adduction-abduction, hip flexion-extension, knee flexion-extesion and ankle dorsi-plantar flexion. Advanced methods are developed to capture the human movement and produce realistic motion simulations. These are used to provide dynamic consistency to the NMS model operation. Pattern recognition and machine learning technology is used to predict the human motor intention from the analysis of EMG signals and integrate context knowledge into the EMG-driven NMS model.
This research develops the technology needed to establish an EMG-driven human-machine interface (HMI) for the simultaneous actuation of multiple joints in a lower limb powered orthosis. This work, indeed, shows for the first time it is possible to use EMG signals to estimate the joint moments simultaneously
produced about multiple DOFs and this is crucial to provide better estimates of muscle force with respect to the state of the art. This thesis also suggests the NMS model can be exploited to address the challenge of autonomous locomotion in musculoskeletal humanoids.
The objective of this work therefore, is to provide effective solutions and readily available software tools to improve the human interaction with robotic assistive devices. This is achieved by advancing research in neuromusculoskeletal modeling to better understand the mechanisms of actuation provided by human muscles.
Understanding these mechanisms is the key to realize human interaction with wearable assistive devices. This work designs and develops the technology for achieving this.Questo lavoro di ricerca presenta un innovativo modello neuromuscoloscheletrico (NMS) dell'arto inferiore umano. Il modello e' fisiologicamente accurato e computazionalmente
efficiente. Utilizza segnali elettromiograci (EMG) acquisiti da 16
muscoli per predire la forza sviluppata da 34 attuatori muscolo-tendinei (MTAs).
Ogni MTA e vincolato a soddisfare i momenti articolari generati rispetto a 4 gradi di liberta: adduzione-abduzione e
flessione-estensione dell'anca, flessione-estensione del ginocchio e flessione plantare-dorsale della caviglia. Sono stati sviluppati metodi avanzati per digitalizzare il movimento umano e creare simulazioni motorie realistiche. Queste vengono utilizzate per assicurare consistenza dinamica durante l'esecuzione del modello NMS. Tecniche di pattern recognition e machine learning
vengono poi utilizzate per predire il tipo di movimento che il soggetto umano vuole compiere attraverso l'analisi dei segnali EMG. Questa ricerca sviluppa gli strumenti necessari per realizzare un interfaccia uomo macchina (HMI) comandata da segnali EMG che consenta l'attuazione simultanea dei giunti articolari in un esoscheletro dell'arto inferiore.
Viene mostrato, infatti, per la prima volta, che e' possibile usare segnali EMG per stimare i momenti articolari prodotti rispetto a piu gradi di liberta e che questo e' fondamentale per ottenere stime corrette della forza muscolare. Questa tesi illustra anche la possibilita di implementare strategie di locomozione per robot
umanoidi dotati di una struttura muscoloscheletrica.
L'obiettivo di questo lavoro e' quindi quello di fornire soluzioni efficaci e strumenti software avanzati per migliorare l'interazione umana con dispositivi robotici di assistenza. Questo e' ottenuto attraverso una ricerca nel campo della modellazzione neuromuscoloscheletrica per comprendere i meccanismi di attuazione propri dei muscoli uniarticolari e biarticolari umani. La comprensione di tali meccanismi rappresenta il punto chiave per lo sviluppo di soluzioni efficaci per il controllo di sistemi assistivi indossabili. Questo lavoro mette a disposizione la tecnologia necessaria per ottenere tali risultati
Erratum: Corrections to Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies (IEEE Transactions on Biomedical Engineering (2016) 63:5 (879-893))
No full textA neuromusculoskeletal modeling toolbox to explore the effect of different neural solutions to human movement
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A lower limb EMG-driven biomechanical model for applications in rehabilitation robotics
No full textCurrent changes in aging demographics poses new challenges: people require to keep their quality of life even after circumstances that threatened their movement and function. This increases the demand for new physical rehabilitation facilities that go beyond the traditional patient-therapist, one-to-one rehabilitation sessions. Two promising solutions rely on virtual reality and on the development of autonomous active orthoses, or exoskeletons. Whatever is the chosen approach, there is a requirement for a robust human-machine interface for the control, able to understand patient's intention and to produce an immediate activation of the device. This paper presents a biomechanical model, a possible solution able to predict joint torque from the surface electromyography signals emitted by muscles during their activation. The main objective of the research is to investigate the benefits and efficacy of this model and to lay down the basis of our current research, whose main goal is to make possible a rehabilitation process either with active orthoses or virtual reality. Experiments involving all the steps of our model demonstrate the viability and effectiveness of our approach
Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.
Full text linkWe propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function
CEINMS: an OpenSim neuromusculoskeletal toolbox to examine how different neural solutions affect muscle forces and joint moment estimates
No full textA theoretical and computational framework for modeling and simulating musculoskeletal stiffness during locomotion
No full textAn EMG-driven musculoskeletal model of the human lower limb for the estimation of muscle forces and moments at the hip, knee and ankle joints in vivo
No full textThis work presents the design of a novel neuromusculoskeletal model (NMS) of the human lower limb to estimate muscle forces and moments about the hip, knee and ankle joints. This research shows it is possible to use electromyographic (EMG) signals recorded from 16 muscles to drive 34 musculotendon actuators and constrain their operation to simultaneously satisfy the production of moments across several degrees of freedom (DOF) including: hip adduction-abduction, hip flexion- extension, knee flexion-extension, ankle dorsi-plantar flexion. Past research proposed the use single-DOF NMS model to estimate muscle forces and joint moments. However, these models do not properly allow muscles to operate with respect to all the DOFs associated to the joints they span. This leads to unrealistic estimations of muscle activation patterns and force production dynamics. Our proposed model was able to generate muscle forces that properly satisfied the moments generated at hip, knee and ankle joints during a variety of dynamic motor tasks
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