Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%

A Subject-Specific EMG-Driven Musculoskeletal Model for the Estimation of Moments in Ankle Plantar-Dorsiflexion Movement

In traditional rehabilitation process, ankle movement ability is only qualitatively estimated by its motion performance, however, its movement is actually achieved by the forces acting on the joints produced by muscles contraction. In this paper, the musculoskeletal model is introduced to provide a more physiologic method for quantitative muscle forces and muscle moments estimation during rehabilitation. This paper focuses on the modeling method of musculoskeletal model using electromyography (EMG) and angle signals for ankle plantar-dorsiflexion (P-DF) which is very important in gait rehabilitation and foot prosthesis control. Due to the skeletal morphology differences among people, a subject-specific geometry model is proposed to realize the estimation of muscle lengths and muscle contraction force arms. Based on the principle of forward and inverse dynamics, difference evolutionary (DE) algorithm is used to adjust individual parameters of the whole model, realizing subject-specific parameters optimization. Results from five healthy subjects show the inverse dynamics joint moments are well predicted with an average correlation coefficient of 94.21% and the normalized RMSE of 12.17%. The proposed model provides a good way to estimate muscle moments during movement tasks

An isovelocity dynamometer method to determine monoarticular and biarticular muscle parameters

This study aimed to determine whether subject-specific individual muscle models for the ankle plantar flexors could be obtained from single joint isometric and isovelocity maximum torque measurements in combination with a model of plantar flexion. Maximum plantar flexion torque measurements were taken on one subject at six knee angles spanning full flexion to full extension. A planar three-segment (foot, shank and thigh), two muscle (soleus and gastrocnemius) model of plantar flexion was developed. Seven parameters per muscle were determined by minimizing a weighted root mean square difference (wRMSD) between the model output and the experimental torque data. Valid individual muscle models were obtained using experimental data from only two knee angles giving a wRMSD score of 16 N m, with values ranging from 11 to 17 N m for each of the six knee angles. The robustness of the methodology was confirmed through repeating the optimization with perturbed experimental torques (±20%) and segment lengths (±10%) resulting in wRMSD scores of between 13 and 20 N m. Hence, good representations of maximum torque can be achieved from subject-specific individual muscle models determined from single joint maximum torque measurements. The proposed methodology could be applied to muscle-driven models of human movement with the potential to improve their validity

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

Mechanical factors affecting the estimation of tibialis anterior force using an EMG-driven modelling approach

The tibialis anterior (TA) muscle plays a vital role in human movement such as walking and running. Overuse of TA during these movements leads to an increased susceptibility of injuries e.g. chronic exertional compartment syndrome. TA activation has been shown to be affected by increases in exercise, age, and the external environment (i.e. incline and footwear). Because activation parameters of TA change with condition, it leads to the interpretation that force changes occur too. However,activation is only an approximate indicator of force output of a muscle. Therefore, the overall aim of this thesis was to investigate the parameters affecting accurate measure of TA force, leading to development of a subject-specific EMG-driven model, which takes into consideration specific methodological issues. The first study investigated the reasons why the tendon excursion and geometric method differ so vastly in terms of estimation of TA moment arm. Tendon length changes during the tendon excursion method, and location of the TA line of action and irregularities between talus and foot rotations during the geometric method, were found to affect the accuracy of TA moment arm measurement. A novel, more valid, method was proposed. The second study investigated the errors associated with methods used to account for plantar flexor antagonist co-contraction. A new approach was presented and shown to be, at worse, equivalent to current methods, but allows for accounting throughout the complete range of motion. The final study utilised the outputs from studies one and two to directly measure TA force in vivo. This was used to develop, and validate, an EMG-driven TA force model. Less error was found in the accuracy of estimating TA force when the contractile component length changes were modelled using the ankle, as opposed to the muscle. Overall, these findings increase our understanding of not only the mechanics associated with TA and the ankle, but also improves our ability to accurately monitor these

Mechanical factors affecting the estimation of tibialis anterior force using an EMG-driven modelling approach

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe tibialis anterior (TA) muscle plays a vital role in human movement such as walking and running. Overuse of TA during these movements leads to an increased susceptibility of injuries e.g. chronic exertional compartment syndrome. TA activation has been shown to be affected by increases in exercise, age, and the external environment (i.e. incline and footwear). Because activation parameters of TA change with condition, it leads to the interpretation that force changes occur too. However,activation is only an approximate indicator of force output of a muscle. Therefore, the overall aim of this thesis was to investigate the parameters affecting accurate measure of TA force, leading to development of a subject-specific EMG-driven model, which takes into consideration specific methodological issues. The first study investigated the reasons why the tendon excursion and geometric method differ so vastly in terms of estimation of TA moment arm. Tendon length changes during the tendon excursion method, and location of the TA line of action and irregularities between talus and foot rotations during the geometric method, were found to affect the accuracy of TA moment arm measurement. A novel, more valid, method was proposed. The second study investigated the errors associated with methods used to account for plantar flexor antagonist co-contraction. A new approach was presented and shown to be, at worse, equivalent to current methods, but allows for accounting throughout the complete range of motion. The final study utilised the outputs from studies one and two to directly measure TA force in vivo. This was used to develop, and validate, an EMG-driven TA force model. Less error was found in the accuracy of estimating TA force when the contractile component length changes were modelled using the ankle, as opposed to the muscle. Overall, these findings increase our understanding of not only the mechanics associated with TA and the ankle, but also improves our ability to accurately monitor these.Headley Court Trust and the Defence Medical Rehabilitation Centre

Assist-as-needed EMG-based control strategy for wearable powered assistive devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Robotic-based gait rehabilitation and assistance using Wearable Powered Assistive Devices (WPADs), such as orthosis and exoskeletons, has been growing in the rehabilitation area to recover and augment the motor function of neurologically impaired subjects. These WPADs should provide a personalized assistance, since physical condition and muscular fatigue modify from patient to patient. In this field, electromyography (EMG) signals have been used to control WPADs given their ability to infer the user’s motion intention. However, in cases of motor disability conditions, EMG signals present lower magnitudes when compared to EMG signals under healthy conditions. Thus, the use of WPADs managed by EMG signals may not have potential to provide the assistance that the patient requires. The main goal of this dissertation aims the development of an Assisted-As-Needed (AAN) EMG-based control strategy for a future insertion in a Smart Active Orthotic System (SmartOs). To achieve this goal, the following elements were developed and validated: (i) an EMG system to acquire muscle activity signals from the most relevant muscles during the motion of the ankle joint; (ii) machine learning-based tool for ankle joint torque estimation to serve as reference in the AAN EMG-based control strategy; and (iii) a tool for real EMG-based torque estimation using Tibialis Anterior (TA) and Gastrocnemius Lateralis (GASL) muscles and real ankle joint angles. EMG system showed satisfactory pattern correlations with a commercial system. The reference ankle joint torque was generated based on predicted reference ankle joint kinematics, walking speed information (from 1 to 4 km/h) and anthropometric data (body height from 1.51 m to 1.83 m and body mass from 52.0 kg to 83.7 kg), using five machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN provided the best performance, predicting the reference ankle joint torque with fitting curves ranging from 74.7 to 89.8 % and Normalized Root Mean Square Errors (NRMSEs) between 3.16 and 8.02 %. EMG-based torque estimation beneficiates of a higher number of muscles, since EMG data from TA and GASL are not enough to estimate the real ankle joint torque.A assistência e reabilitação robótica usando dispositivos de assistência ativos vestíveis (WPADs), como ortóteses e exosqueletos, tem crescido na área da reabilitação com o fim de recuperar e aumentar a função motora de sujeitos com alterações neurológicas. Estes dispositivos devem fornecer uma assistência personalizada, uma vez que a condição física e a fadiga muscular variam de paciente para paciente. Nesta área, sinais de eletromiografia (EMG) têm sido usados para controlar WPADs, dada a sua capacidade de inferir a intenção de movimento do utilizador. Contudo, em casos de deficiência motora, os sinais de EMG apresentam menor amplitude quando comparados com sinais de EMG em condições saudáveis e, portanto, o uso de WPADs geridos por sinais de EMG pode não oferecer a assistência que o paciente necessita. O principal objetivo desta dissertação visa o desenvolvimento de uma estratégia de controlo baseada em EMG capaz de fornecer assistência quando necessário, para futura integração num sistema ortótico ativo e inteligente (SmartOs). Para atingir este objetivo foram desenvolvidos e validados os seguintes elementos: (i) sistema de EMG para adquirir sinais de atividade muscular dos músculos mais relevantes no movimento da articulação do tornozelo; (ii) ferramenta de machine learning para estimação do binário da articulação do tornozelo para servir como referência na estratégia de controlo; e (iii) ferramenta de estimação do binário real do tornozelo considerando sinais de EMG dos músculos Tibialis Anterior (TA) e Gastrocnemius Lateralis (GASL) e ângulo real do tornozelo. O sistema de EMG apresentou correlações satisfatórias com um sistema comercial. O binário de referência para o tornozelo foi gerado com base no ângulo de referência da mesma articulação, velocidade de marcha (de 1 até 4 km/h) e dados antropométricos (alturas de 1.51 m até 1.83 e massas de 52.0 kg até 83.7 kg), usando cinco algoritmos de machine learning: Support Vector Machine, Random Forest, Multilayer Perceptron, Long-Short Term Memory e Convolutional Neural Network. CNN apresentou a melhor performance, prevendo binários de referência do tornozelo com um fit entre 74.7 e 89.8 % e Normalized Root Mean Square Errors (NRMSE) entre 3.16 e 8.02 %. A estimativa do torque com base em sinais de EMG requer a inclusão de um maior número de músculos, uma vez que sinais de EMG dos músculos TA e GASL não foram suficientes