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

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Signal in Human Motor Unsteadiness: Determining the Action and Activity of Muscles.

When the human skeleton is moved by muscles, the resulting movement is inherently unsteady. This work introduces two new approaches for using motor unsteadiness as a window into central nervous system function. The first approach, termed "Force Covariance Mapping" (FCM), demonstrates experimentally that there can be systematic differences in how forces exerted by limbs fluctuate depending on the direction of intended movement. Forces exerted in different directions by the human index finger were measured using a sensitive load cell. In certain directions of intended movement, forces were found to fluctuate in magnitude only, while in other directions of intended movement, forces were found to fluctuate in both direction and magnitude. Along with electromyographic (EMG) recordings and biomechanical estimates, force fluctuation data indicates that the central nervous system uses different muscular control strategies for different directions of intended movement: some movement directions are generated primarily by single muscles while others involve cooperation among multiple muscles. The second approach, termed "EMG-weighted averaging" (EWA), couples measures of electrical activity with concurrent motor unsteadiness to estimate the direction of mechanical contribution (action) for a muscle of interest. EWA tracks how exerted forces fluctuate after EMG in a particular muscle increases. This approach allows the exploration of complex neuromechanical phenomena "in vivo". EWA was applied to forces exerted isometrically by the human index finger and EMG data from two muscles: the first dorsal interosseous (FDI) and extensor indicis proprius (EIP) muscles. EWA estimates for the action direction of these muscles were found to change depending on the intended movement direction. These changes could relate to several hypotheses of muscle action, including differential control of motor units within a muscle as well as nonlinear summation of force among muscles. In addition, this work presents novel predictive equations describing spike-triggered averaging, a commonly-used neuroscience tool for understanding motor unit function that works by coupling motor unit electrical discharges with motor unsteadiness. Studying human motor unsteadiness, in the detail presented in this work, holds great promise for increasing our understanding human motor function and pathology.Ph.D.Applied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60825/1/jkutch_1.pd

Subject-Specific Computational Musculoskeletal Modeling of Human Trunk in Lifting : Role of Age, Sex, Body Weight and Body Height

Résumé Les troubles musculosquelettiques sont parmi les problèmes de santé les plus fréquents et les plus coûteux au monde. Les maux de dos figurent en deuxième position sur la liste des états chroniques les plus répandus au Canada et quatre adultes sur cinq souffriront de lombalgie un jour ou l’autre de leur vie. Les efforts excessifs sur la colonne vertébrale constituent l’un des facteurs de risque potentiels de lombalgie et peuvent initier ou générer de la douleur et de la dégénérescence des disques. À cet effet, plusieurs études s’accordent pour affirmer qu’une estimation juste des charges vertébrales est utile pour une prévention efficace des blessures et pour des programmes de réadaptation appropriés. Toutefois, il n’existe pas de méthodes directes pour mesurer les charges vertébrales et de plus, toutes les méthodes indirectes (comme la mesure de la pression intradiscale – PID – et l’estimation au moyen de prothèse discale instrumentée) sont invasives et limitées. Les modèles musculosquelettiques (MS) offrent toutefois une alternative intéressante en estimant de manière non invasive, économique et précise les forces musculaires, les charges vertébrales ainsi que la stabilité de la colonne vertébrale en tenant compte des différences individuelles. Dans cette thèse, un modèle MS du tronc par éléments finis (EF) guidé par la cinématique a été mis à niveau. L’architecture des origines et insertions musculaires a été améliorée, une unité vertébrale comprenant un disque déformable a été ajoutée (T11-T12) et un nouvel algorithme de mise à l’échelle a été introduit afin d’explorer les effets du sexe, de l’âge, du poids et de la taille sur la biomécanique et les charges appliquées sur la colonne vertébrale. Au moyen de données issues d’imageries médicales et à partir de principes biomécaniques, l’algorithme de mise à l’échelle a permis d’ajuster l’architecture musculaire (les bras de levier des muscles et les aires transverses), la géométrie et les propriétés passives ligamentaires de la colonne vertébrale ainsi que la charge gravitationnelle, le tout en fonction du sexe, de l’âge, du poids et de la taille. Une analyse de sensibilité a été effectuée au moyen d’une analyse factorielle multiple. Les données d’entrées du modèle (sexe, âge, poids et taille) ont été modifiées à l’intérieur de plages physiologiques (sexe : femme et homme ; âge : 35 à 60 ans ; poids : 50 à 120 kg ; taille : 150 à 190 cm) tandis que le modèle personnalisé par EF était guidé par une cinématique spécifique à l’âge et au sexe lors de différentes tâches de flexion avant avec ou sans charges manuelles. Des graphiques illustrant les effets principaux et des analyses de variance ont été utilisés pour évaluer les effets des données d’entrées sur le chargement au dos. Le poids du corps a été le facteur le plus influent, en expliquant 99 % du chargement lombaire en compression et 96 % de celui en cisaillement, alors que les effets de la taille, du sexe et de l’âge (<5 %) étaient minimes. Aussi, pour des poids et des tailles similaires aux hommes, les femmes supportaient généralement des charges plus importantes au dos (5 % en compression, 9 % en cisaillement) La prévalence de l’obésité, dont l’indice de masse corporelle (IMC) dépasse les 30 kg/m2, est en croissance constante dans les pays développés comme dans les pays en voie de développement et a atteint un seuil critique « d’épidémie mondiale ». Bien que l’obésité soit associée à plusieurs problèmes au dos (ex. : dégénération discale, fractures vertébrales, maux de dos), le rôle de la biomécanique dans les problèmes liés à l’obésité demeure inconnu. La distribution du tissu adipeux varie considérablement d’un individu obèse à un autre, et ce, même dans les cas d’IMC et de poids identiques. On retrouve différentes formes d’obésité, dont celle « en pomme » et celle « en poire » (androïde et gynoïde respectivement). Le rôle de l’obésité et des formes d’obésité sur les charges supportées par la colonne vertébrale et sur les fractures de compression vertébrale a été étudié à l’aide du modèle personnalisé mis à jour. Trois formes distinctes d’obésité (correspondant à une taille de circonférence minimale, moyenne et maximale) pour un poids et un IMC identiques ont été simulées au moyen de mensurations anthropométriques obtenues à partir de 5852 individus obèses et d’une analyse par composantes principales. L’obésité a des conséquences significatives sur le chargement lombaire : la compression sur L4-L5 a bondi de 16 % (2820 N vs 3350 N) pour une flexion avant sans charges lorsque l’IMC a augmenté de 31 kg/m2 à 39 kg/m2. Dans une comparaison entre une taille de circonférence minimale (obésité en forme de poire) et celle d’une circonférence maximale (obésité en forme de pomme), le chargement lombaire a subi une augmentation similaire à celle d’ajouter 20 kg de poids supplémentaire, ainsi qu’un risque de fracture de fatigue vertébrale sept fois plus élevé. En somme, l’obésité et les formes d’obésité ont une influence considérable sur la biomécanique de la colonne vertébrale, et donc, devraient être prises en compte lors d’une modélisation spécifique aux sujets. En plus de servir à l’évaluation de la force maximale du tronc et à la normalisation de l’électromyographie (EMG), les contractions musculaires volontaires maximales (CVM) peuvent être utilisées pour calibrer et valider les modèles MS. La performance du modèle MS personnalisé a été étudiée en comparant les activités musculaires estimées avec les EMG durant diverses tâches de CVM. Le stress musculaire maximal des muscles du tronc a également été calculé pour chaque sujet. Ce dernier a varié considérablement entre différents sujets et groupes musculaires. Le muscle grand droit et le muscle oblique externe de l’abdomen ont eu, respectivement, le plus petite (0,40 ±0,22 MPa) et la plus grande valeur (0,99 ±0,29 MPa) de stress musculaire maximal parmi les groupes de muscles. Pour les CVM en flexion et en extension, les activités musculaires estimées correspondaient adéquatement avec les EMG. Cependant, cette correspondance était faible pour les CVM en flexion latérale et rotations axiales. Le chargement lombaire des femmes était en général plus faible que celui des hommes. Les charges vertébrales maximales lors des CVM ont été obtenues lors des efforts en extension (compression d’environ 6000 N à L5-S1) tandis que les plus faibles ont été enregistrées en flexion avant (compression d’environ 3000 N à L5-S1) ; les participants ont subi des chargements lombaires assez importants durant des CVM en flexion latérale et rotation axiale. (5500 N en compression et 1700 N en cisaillement). La prédiction exacte du stress musculaire maximal et l’évaluation complète de la performance d’un modèle MS nécessitent la prise en compte des tâches de CVM dans toutes les directions et l’application des moments dans les plans principaux et couplés du modèle. Une simulation adéquate des ligaments passifs de la colonne vertébrale, l’une des composantes majeures d’un modèle MS du tronc, est d’une importance capitale. Les modèles détaillés d’EF peuvent capturer avec précision les réactions non linéaires et temporelles de la colonne vertébrale. Toutefois, en raison des coûts de calcul importants des modèles détaillés d’éléments finis, des modèles simplifiés (c.-à-d. à partir de joints sphériques et de poutres ayant des propriétés passives linéaires ou non linéaires) sont couramment utilisés dans les principaux modèles MS. Par conséquent, la précision et la validité de l’utilisation de modèles simplifiés et de leur positionnement antéro-postérieur dans l’estimation de la cinématique de la colonne vertébrale ligamentaire, des forces musculaires et des charges spinales ont été étudiées. Contrairement aux poutres, les articulations de type sphérique négligeaient les degrés de liberté en translation et n’ont pas réussi à prédire la cinématique de la colonne lombaire avec précision, surtout dans la direction craniocaudale. Les poutres et les joints sphériques non linéaires ont prédit de manière satisfaisante la PID en comparaison avec les mesures in vivo d’activités physiques variées. En revanche, l’utilisation des poutres ou des joints sphériques aux propriétés linéaires passives n’a donné que des résultats valides que pour des angles de flexion d’amplitude faible ou modérée (<40 o). En négligeant les propriétés passives des articulations (joints sphériques sans frottement), on a considérablement augmenté le chargement lombaire en compression et en cisaillement, de 32 % et 63 % respectivement. Le déplacement postérieur (de 8 mm) d’une articulation simplifiée a augmenté les charges lombaires (en compression et en cisaillement) d’environ 20 %, tandis qu’un déplacement vers l’avant (2 mm) a diminué de 10 % la compression et de 18 % la force de cisaillement. De plus, un déplacement postérieur du modèle simplifié a réduit la force passive des muscles agonistes, et ce, tout en augmentant leurs composantes actives. Les modèles d’articulation simplifiés avec des propriétés passives non linéaires devraient se situer entre -2 à +4 mm (+ : postérieur) du centre du disque pour des prédictions justes des forces sur la colonne vertébrale et des forces musculaires actives/passives. L’obtention de résultats valides à l’aide des modèles MS exige des moyens considérables comme une collecte complète de données (ex. : cinématiques, EMG), un laboratoire bien équipé et une formation suffisante. Par ailleurs, des équations de régression faciles à utiliser ont précédemment été mises au point pour estimer le chargement lombaire. Cependant, ces équations ne tiennent pas compte de l’anthropométrie des participants (ex. : poids et taille) fondée sur une approche physiologique, et elles négligent souvent l’asymétrie de la tâche. Dans cette partie de l’étude, des équations de régression spécifiques aux sujets ont été développées pour prédire le chargement lombaire (à L4-L5 et L5-S1) en utilisant un modèle d’EF guidé par la cinématique. L’exactitude de ce modèle et des équations de régression ont été évaluées en comparant les activités musculaires estimées par le modèle avec ceux obtenus au moyen de l’EMG et des PDI calculées avec ceux de la littérature existante. Les valeurs estimées de la PDI spécifiques aux sujets présentaient des corrélations élevées avec les résultats d’études in vivo lors de tâches symétriques et asymétriques (R2=0.82). Dans le cas des tâches symétriques, les estimations d’activité musculaire étaient raisonnablement comparables avec les résultats d’EMG. Toutefois, dans les tâches asymétriques, les estimations étaient moyennement (muscles du dos) ou faiblement (muscles de l’abdomen) en accord avec les EMG. En somme, les équations de régression développées peuvent être utilisées dans le but d’estimer le chargement lombaire dans des tâches de levage symétriques et asymétriques. Ces équations personnalisées pourraient servir à l’évaluation des risques de blessure au dos lors d’activités de manutention. En résumé, un modèle MS d’EF guidé par la cinématique, mis à jour par une architecture musculaire améliorée, un disque déformable additionnel (T11-T12) et un nouvel algorithme de mise à l’échelle a été utilisé pour examiner la biomécanique personnalisée de la colonne vertébrale. En personnalisant tous les paramètres du modèle MS (les bras de levier des muscles, les aires transverses musculaires, le chargement gravitationnel, la géométrie de la colonne, les propriétés passives et la cinématique de la colonne vertébrale), et en effectuant une analyse de sensibilité sur les données d’entrées du modèle (sexe, âge, taille et poids), il a été démontré que le poids d’une personne influence nettement les forces de chargement subies par la colonne vertébrale, alors que l’influence des autres facteurs était plutôt faible. Deux formes distinctes d’obésité ont été reconstituées à partir d’un ensemble de données anthropométriques disponibles dans la littérature. Les résultats ont établi que l’obésité et les formes d’obésité (formes en pomme ou en poire) affectent, toutes les deux, les forces sur la colonne vertébrale ainsi que le risque de fracture de fatigue vertébrale. Lors de tâches de CVM (en extension, en flexion, en flexion latérale et en rotation axiale), les grandeurs du stress musculaire variaient substantiellement parmi les sujets et différents groupes musculaires. Dans le cas des CVM en flexion et en extension, les valeurs prédites d’activité musculaire par le modèle personnalisé étaient près des EMG enregistrés, alors que les prédictions concernant les CVM en rotation axiale et en flexion latérale n’avaient pas la même exactitude. Des poutres et des joints sphériques ayant des propriétés non linéaires (d’une position variant de -2 à +4 mm [+ : postérieur] du centre des disques) prédisait avec exactitudes les cinématiques de la colonne vertébrale, le chargement lombaire et les activités musculaires. Par contre, les modèles articulaires qui avaient des propriétés linéaires ou qui n’avaient pas de degrés de liberté en translation détérioraient l’exactitude des prédictions. Enfin, des équations de régression faciles à utiliser ont été mises au point dans le but de prédire les forces de compression et de cisaillement subies par la colonne vertébrale (aux niveaux L4-L5 et L5-S1) lors de tâches symétriques et asymétriques. Les équations personnalisées ont correctement estimé les valeurs de PID en comparant les valeurs calculées avec les résultats mesurés in vivo retrouvés dans la littérature. Lors de plusieurs tâches symétriques et asymétriques, les valeurs estimées des activités musculaires étaient moyennement (pour les muscles du dos) à faiblement (pour les muscles abdominaux) comparables avec les EMG enregistrés des participants. Par conséquent, les équations de régression proposées peuvent être utilisées pour évaluer les risques de blessures lors d’activités de manutention. ---------- Abstract Musculoskeletal disorders are one the most frequent and costly disabilities in the world. Back problems are the second most common chronic condition in Canada. Four out of five adults experience low back pain in their lifetime. As one of the potential risk factors of back pain, excessive loads on the spine can initiate and promote disc degeneration and pain, so accurate estimation of spinal loads are helpful in designing effective prevention, evaluation, and treatment programs. There is no direct method to measure spinal loads, and all indirect methods (intradiscal pressure – IDP – and instrumented vertebral replacement) are invasive and scarce. Alternatively, musculoskeletal (MS) models with physiological scaling algorithms economically and accurately estimate muscle forces, spinal loads and spinal stability margin by taking into account individual differences. An existing kinematics driven (KD) finite element (FE) MS musculoskeletal model of the trunk has been upgraded in this work by refining the muscle architecture, by adding a new deformable disc level (T11-T12), and by introducing a novel scaling algorithm to explore likely effects of sex, age, body weight (BW) and body height (BH) on spine biomechanics and spinal loads. By using imaging datasets and biomechanical principles, the scaling algorithm adjusted the muscle architecture (muscle moment arms and cross-sectional areas), spine geometry, passive properties of the ligamentous spine and gravity loads based on subject’s sex, age, BH and BW. To perform a sensitivity analysis in a full-factorial design, model inputs (i.e., sex, age, BH and BW) were altered within physiological ranges (sex: female and male; age: 35-60 years; BH: 150-190 cm; BW:50-120 kg) while the personalized KD-FE model of the trunk was driven with sex- and age-specific kinematics during different forward flexion tasks with and without a hand-load. Main effect plots and the analysis of variance were employed to investigate effects of inputs on spinal loads. As the most influential factor, BW contributed 99% to compression and 96% to shear spinal loads while effects of BH, sex and age (<5%) remained much smaller. At identical BH, BW and waist circumference, females had slightly greater spinal loads (5% in compression; 9% in shear). The prevalence of obesity (body mass index; BMI>30 kg/m2) is rising in both developed and developing countries, and has reached “global epidemic” proportions. Although obesity has been associated with various back problems (e.g., disc degeneration, vertebral fracture and back pain),the likely role of biomechanics in obesity-related back problems is still unknown. At identical BMI and BW, fat distribution varies substantially from one obese individual to another. Different obesity types have qualitatively been described as apple- and pear-shaped (or android and gynoid). Therefore, effects of obesity and obesity shapes on spinal loads and vertebral compression fracture were investigated by using the upgraded subject-specific model. At identical BW and BH, three distinct obesity shapes (corresponding to minimum, average and maximum waist circumferences) were reconstructed by using available anthropometric measurements of 5852 obese individuals and principal component analysis. Obesity markedly affected spinal loads; L4-L5 compression increased by 16% (2820 N vs 3350 N) in forward flexion without a hand-load when BMI increased from 31 kg/m2 to 39 kg/m2. Greater waist circumferences (apple-shaped obesity) in comparison with smaller waist circumferences (pear-shaped obesity) increased spinal loads to the extent of gaining 20 kg additional BW and the risk of vertebral fatigue fracture by up to ~7 times. Therefore, both obesity and obesity shapes substantially affected spine biomechanics and should be taken into account in subject-specific modeling of the spine. Apart from serving in the trunk strength quantification and electromyography (EMG) normalization, maximum voluntary exertions (MVEs) can be used to calibrate and validate MS models. The performance of the current upgraded subject-specific MS model was investigated by comparing estimated muscle activities with reported EMGs during various MVE tasks. Maximum muscle stresses of trunk muscles were also calculated for each subject individually. Estimated maximum muscle stresses varied substantially among subjects and different muscle groups; rectus abdominis and external oblique had the smallest (0.40±0.22 MPa) and largest (0.99±0.29 MPa) maximum muscle stresses, respectively. In sagittal symmetric MVEs (extension and flexion), estimated muscle activities were found in satisfactory agreement with measured reported EMGs while in lateral and axial MVEs, the agreement was rather weak. Females in general had smaller spinal loads. Peak spinal loads were obtained in extension MVE (~6000 N compression at L5-S1) while flexion MVE yielded the smallest spinal loads (~3000 N compression at L5-S1); subjects experienced rather large spinal loads (5500 N in compression and 1700 N in shear) under lateral and axial MVEs. Accurate prediction of maximum muscle stresses and comprehensive evaluation of the performance of a MS model require the consideration of MVE tasks in all directions with the application of both primary and coupled moments to the model. Accurate simulation of the passive ligamentous spine, as one of the integral components of a trunk MS model, is of great importance. Detailed FE models can accurately capture nonlinear and time-dependent responses of the spine; however, due to the significant computational costs of detailed FE models, simplified models (i.e., spherical joints/beams with linear/nonlinear passive properties) are commonly used in the trunk MS models. Therefore, the accuracy and validity of using simplified models and their anterior-posterior positioning in estimating kinematics of the ligamentous spine, muscle forces and spinal loads were investigated. Unlike beam elements, spherical joints overlooked translational degrees of freedom and failed to accurately predict kinematics of the lumbar spine particularly in the cranial-caudal direction. Nonlinear shear deformable beams and spherical joints were found to satisfactorily predict IDPs in comparison with in vivo measurements during various activities. In contrast, using beams or spherical joints with linear passive properties yielded valid results only in small to moderate flexion angles (<40o). Neglecting passive properties of joints (frictionless spherical joints) substantially increased compression and shear spinal loads by 32% and 63%. Shifting a simplified joint posteriorly (by 8 mm) increased spinal loads (compression and shear) by ~20% while an anterior shift (by 2 mm) decreased spinal loads by 10% and 18% in compression and shear directions. Moving simplified joint models posteriorly reduced also passive muscle forces of agonist muscles while increasing their active components. Simplified joint models with nonlinear passive properties should be located in -2 to +4 mm (+: posterior) range from the disc center for accurate predictions of spinal loads and active/passive muscle forces. Obtaining reasonably accurate results by MS models requires comprehensive data collection (e.g., kinematics, EMG), equipped laboratory, and sufficient training. Alternatively, easy to use regression equations have previously been developed to estimate spinal loads, but they do not take account of personalized anthropometric factors (e.g., BW and BH) based on a physiological approach and often overlook task asymmetry. Thus, in this work, subjects-specific regression equations were developed to predict spinal loads at lower spinal levels (L4-L5 and L5-S1) by using the upgraded KD-FE model, and the Accuracy of the model and regression equations were subseq

Model-free Optimization of Trajectory And Impedance Parameters on Exercise Robots With Applications To Human Performance And Rehabilitation

This dissertation focuses on the study and optimization of human training and its physiological effects through the use of advanced exercise machines (AEMs). These machines provide an invaluable contribution to advanced training by combining exercise physiology with technology. Unlike conventional exercise machines (CEMs), AEMs provide controllable trajectories and impedances by using electric motors and control systems. Therefore, they can produce various patterns even in the absence of gravity. Moreover, the ability of the AEMs to target multiple physiological systems makes them the best available option to improve human performance and rehabilitation. During the early stage of the research, the physiological effects produced under training by the manual regulation of the trajectory and impedance parameters of the AEMs were studied. Human dynamics appear as not only complex but also unique and time-varying due to the particular features of each person such as its musculoskeletal distribution, level of fatigue,fitness condition, hydration, etc. However, the possibility of the optimization of the AEM training parameters by using physiological effects was likely, thus the optimization objective started to be formulated. Some previous research suggests that a model-based optimization of advanced training is complicated for real-time environments as a consequence of the high level of v complexity, computational cost, and especially the many unidentifiable parameters. Moreover, a model-based method differs from person to person and it would require periodic updates based on physical and psychological variations in the user. Consequently, we aimed to develop a model-free optimization framework based on the use of Extremum Seeking Control (ESC). ESC is a non-model based controller for real-time optimization which its main advantage over similar controllers is its ability to deal with unknown plants. This framework uses a physiological effect of training as bio-feedback. Three different frameworks were performed for single-variable and multi-variable optimization of trajectory and impedance parameters. Based on the framework, the objective is achieved by seeking the optimal trajectory and/or impedance parameters associated with the orientation of the ellipsoidal path to be tracked by the user and the stiffness property of the resistance by using weighted measures of muscle activations

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field

A Human Motor Control Framework based on Muscle Synergies

In spite of the complexities of the human musculoskeletal system, the central nervous system has the ability to orchestrate difficult motor tasks. Many researchers have tried to understand how the human nervous system works. Yet, our knowledge about the integration of sensory information and motor control is incomplete. This thesis presents a mathematical motor control framework that is developed to give the scientific community a biologically-plausible feedback controller for fast and efficient control of musculoskeletal systems. This motor control framework can be applied to musculoskeletal systems of various complexities, which makes it a viable tool for many predictive musculoskeletal simulations, assistive device design and control, and general motor control studies. The most important feature of this real-time motor control framework is its emphasis on the intended task. In this framework, a task is distinguished by the kinematic variables that need to be controlled. For example, in a reaching task, the task variables are the position of the hand (individual joint angles are irrelevant to the reaching task). Consequently, the task space is defined as the subspace that is formed by all the controlled variables. This motor control framework employs a hierarchical structure to speed up the calculations while maintaining high control efficiency. In this framework, there is a high-level controller, which deals with path planning and error compensation in the task space. The output of this task space controller is the acceleration vector in the task space, which needs to be fulfilled by muscle activities. The fast and efficient transformation of the task space accelerations to muscle activities in real-time is a main contribution of this research. Instead of using optimization to solve for the muscle activations (the usual practice in the past), this acceleration-to-activation (A2A) mapping uses muscle synergies to keep the computations simple enough to be real-time implementable. This A2A mapping takes advantage of the known effect of muscle synergies in the task space, thereby reducing the optimization problem to a vector decomposition problem. To make the result of the A2A mapping more efficient, the novel concept of posture-dependent synergies is introduced. The validity of the assumptions and the performance of the motor control framework are assessed using experimental trials. The experimental results show that the motor control framework can reconstruct the measured muscle activities only using the task-related kinematic/dynamic information. The application of the motor control framework to feedback motion control of musculoskeletal systems is also presented in this thesis. The framework is applied to musculoskeletal systems of various complexities (up to four-degree-of-freedom systems with 15 muscles) to show its effectiveness and generalizability to different dimensions. The control of functional electrical stimulation (FES) is another important application of my motor control framework. In FES, the muscles are activated by external electrical pulses to generate force, and consequently motion in paralysed limbs. There exists no feedback FES controller of upper extremity movements in the literature. The proposed motor control model is the first feedback FES controller that can be used for the control of reaching movements to arbitrary targets. Experimental results show that the motor control model is fast enough and accurate enough to be used as a practical motion controller for FES systems. Using such a biologically-plausible motor control model, it is possible to control the motion of a patient's arm (for example a stroke survivor) in a natural way, to accelerate recovery and improve the patient's quality of life

A Human Motor Control Framework based on Muscle Synergies

In spite of the complexities of the human musculoskeletal system, the central nervous system has the ability to orchestrate difficult motor tasks. Many researchers have tried to understand how the human nervous system works. Yet, our knowledge about the integration of sensory information and motor control is incomplete. This thesis presents a mathematical motor control framework that is developed to give the scientific community a biologically-plausible feedback controller for fast and efficient control of musculoskeletal systems. This motor control framework can be applied to musculoskeletal systems of various complexities, which makes it a viable tool for many predictive musculoskeletal simulations, assistive device design and control, and general motor control studies. The most important feature of this real-time motor control framework is its emphasis on the intended task. In this framework, a task is distinguished by the kinematic variables that need to be controlled. For example, in a reaching task, the task variables are the position of the hand (individual joint angles are irrelevant to the reaching task). Consequently, the task space is defined as the subspace that is formed by all the controlled variables. This motor control framework employs a hierarchical structure to speed up the calculations while maintaining high control efficiency. In this framework, there is a high-level controller, which deals with path planning and error compensation in the task space. The output of this task space controller is the acceleration vector in the task space, which needs to be fulfilled by muscle activities. The fast and efficient transformation of the task space accelerations to muscle activities in real-time is a main contribution of this research. Instead of using optimization to solve for the muscle activations (the usual practice in the past), this acceleration-to-activation (A2A) mapping uses muscle synergies to keep the computations simple enough to be real-time implementable. This A2A mapping takes advantage of the known effect of muscle synergies in the task space, thereby reducing the optimization problem to a vector decomposition problem. To make the result of the A2A mapping more efficient, the novel concept of posture-dependent synergies is introduced. The validity of the assumptions and the performance of the motor control framework are assessed using experimental trials. The experimental results show that the motor control framework can reconstruct the measured muscle activities only using the task-related kinematic/dynamic information. The application of the motor control framework to feedback motion control of musculoskeletal systems is also presented in this thesis. The framework is applied to musculoskeletal systems of various complexities (up to four-degree-of-freedom systems with 15 muscles) to show its effectiveness and generalizability to different dimensions. The control of functional electrical stimulation (FES) is another important application of my motor control framework. In FES, the muscles are activated by external electrical pulses to generate force, and consequently motion in paralysed limbs. There exists no feedback FES controller of upper extremity movements in the literature. The proposed motor control model is the first feedback FES controller that can be used for the control of reaching movements to arbitrary targets. Experimental results show that the motor control model is fast enough and accurate enough to be used as a practical motion controller for FES systems. Using such a biologically-plausible motor control model, it is possible to control the motion of a patient's arm (for example a stroke survivor) in a natural way, to accelerate recovery and improve the patient's quality of life

Strategies for control of neuroprostheses through Brain-Machine Interfaces

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 145-153).The concept of brain controlled machines sparks our imagination with many exciting possibilities. One potential application is in neuroprostheses for paralyzed patients or amputees. The quality of life of those who have extremely limited motor abilities can potentially be improved if we have a means of inferring their motor intent from neural signals and commanding a robotic device that can be controlled to perform as a smart prosthesis. In our recent demonstration of such Brain Machine Interfaces (BMIs) monkeys were able to control a robot arm in 3-D motion directly, due to advances in accessing, recording, and decoding electrical activity of populations of single neurons in the brain, together with algorithms for driving robotic devices with the decoded neural signals in real time. However, such demonstrations of BMI thus far have been limited to simple position control of graphical cursors or robots in free space with non-human primates. There still remain many challenges in reducing this technology to practice in a neuroprosthesis for humans. The research in this thesis introduces strategies for optimizing the information extracted from the recorded neural signals, so that a practically viable and ultimately useful neuroprosthesis can be achieved. A framework for incorporating robot sensors and reflex like behavior has been introduced in the form of Continuous Shared Control. The strategy provides means for more steady and natural movement by compensating for the natural reflexes that are absent in direct brain control. The Muscle Activation Method, an alternative decoding algorithm for extracting motor parameters from the neural activity, has been presented.(cont.) The method allows the prosthesis to be controlled under impedance control, which is similar to how our natural limbs are controlled. Using this method, the prosthesis can perform a much wider range in of tasks in partially known and unknown environments. Finally preparations have been made for clinical trials with humans, which would signify a major step in reaching the ultimate goal of human brain operated machines.by Hyun K. Kim.Ph.D