Theoretical mechanical benefits of biarticular muscles include reduced displacements and force potentiating shifts in linear velocities during multi-joint coupled motions. A cadaveric model was developed to compute muscle kinematics of biceps femoris (BFL and BFS) during four classes of coupled knee and hip joint motion, as well as running and walking gait (Six subjects, Vicon Motion Analysis). The examples of the classes of motion were: KEHE-jump (knee extension and hip extension), KFHF-tuck (knee flexion and hip flexion), KFHE-kick (knee flexion and hip extension), and KEHF-paw (knee extension and hip flexion). BFL peak and mean velocity shifts relative to BFS were seen in all four coupling classes (p<0.05) and the majority of the gait subclasses (p<0.05). Muscle displacements were larger in BFL for both KFHE-paw and KEHF-kick (p<0.05), smaller in KFHF-tuck (p<0.05), but not significantly different in KEHE-jump or during most of the running gait subclasses, except for during KFHE-late mid stance and KEHF-mid swing, where they were larger for BFL (p<0.05). The mechanical benefits associated with BFL velocity shift relative to BFs were identified in KFHF, KEHF motions, and certain subclasses of gait. In contrast, there were potential mechanical detriments due to velocity shift relative to BFs in the KEHE-jump, KFHE-paw, and the majority of KEHE and KFHE subclasses in both gait cycles. The possible mechanical benefits associated with displacement conservation of BFL relative to BFs would be realized in KFHF-tuck jump, but not during KEHE-jump and the gait cycle subclasses. The findings of this study reveal both mechanical benefits and detriments of biarticular muscles, and have immediate implications for neural control of biarticular muscles during movement.

Theoretical mechanical benefits of biarticular muscles include reduced displacements and force potentiating shifts in linear velocities during multi-joint coupled motions. A cadaveric model was developed to compute muscle kinematics of biceps femoris (BFL and BFS) during four classes of coupled knee and hip joint motion, as well as running and walking gait (Six subjects, Vicon Motion Analysis). The examples of the classes of motion were: KEHE-jump (knee extension and hip extension), KFHF-tuck (knee flexion and hip flexion), KFHE-kick (knee flexion and hip extension), and KEHF-paw (knee extension and hip flexion). BFL peak and mean velocity shifts relative to BFS were seen in all four coupling classes (p<0.05) and the majority of the gait subclasses (p<0.05). Muscle displacements were larger in BFL for both KFHE-paw and KEHF-kick (p<0.05), smaller in KFHF-tuck (p<0.05), but not significantly different in KEHE-jump or during most of the running gait subclasses, except for during KFHE-late mid stance and KEHF-mid swing, where they were larger for BFL (p<0.05). The mechanical benefits associated with BFL velocity shift relative to BFs were identified in KFHF, KEHF motions, and certain subclasses of gait. In contrast, there were potential mechanical detriments due to velocity shift relative to BFs in the KEHE-jump, KFHE-paw, and the majority of KEHE and KFHE subclasses in both gait cycles. The possible mechanical benefits associated with displacement conservation of BFL relative to BFs would be realized in KFHF-tuck jump, but not during KEHE-jump and the gait cycle subclasses. The findings of this study reveal both mechanical benefits and detriments of biarticular muscles, and have immediate implications for neural control of biarticular muscles during movement.

For years, the human back has puzzled researchers with the complex behaviors it presents. Principally, the internal forces produced by back muscles have not been determined accurately. Two different approaches have historically been taken to predict muscle forces. The first relies on electromyography (EMG), while the second attempts to predict muscle responses using mathematical models. Three such predictive models are compared here. The models are Sum of Cubed Intensities, Artificial Neural Networks, and Distributed Moment Histogram. These three models were adapted to run using recently published descriptions of the lower back anatomy. To evaluate their effectiveness, the models were compared in terms of their fit to a muscle activation database including 14 different muscles. The database was collected as part of this experiment, and included 8 participants (4 male and 4 female) with similar height and weight. The participants resisted loads applied to their torso via a harness. Results showed the models performed poorly (average R2's in the 0.40's), indicating that further improvements are needed in our current low back muscle activation modeling techniques. Considerable discrepancies were found between internal moments (at L3/L4) determined empirically and measured with a force plate, indicating that the maximum muscle stress selected and/or the anatomy used were faulty. The activation pattern database collected also fills a gap in the literature by considering static loading patterns that had not been systematically varied before. / Master of Science

La stimulation électrique fonctionnelle (SEF) a le potentiel de fournir une amélioration active aux blessés médullaires en termes de mobilité, de stabilité et de prévention des effets secondaires.Dans le domaine des système SEF pour les membres inférieurs, le couple articulaire adéquat doit être fournie de façon appropriée pour effectuer le mouvement prévu et maintenir l'équilibre postural. Toutefois, les changements d'état du muscle tels que la fatigue musculaire est une cause majeure qui dégrade ses performances. En outre, la plupart des patients, dont la blessure médullaire est complète, n'ont pas le retour sensorielle qui permet de détecter la fatigue et les capteurs de couples in-vivo ne sont pas disponible à l'heure actuelle. Les systèmes conventionnels de commande SEF sont soit en boucle ouverte ou pas assez robustes aux changements d'état du muscle. L'objectif de cette thèse est le développement de la prédiction du couple articulaire et la commande en boucle fermée afin d'améliorer les performances de la commande SEF en termes de précision, de robustesse et de sécurité pour les patients.Afin de prédire le couple articulaire induit de la SEF, l'électromyographie (EMG) induit est utilisé pour corréler l'activité musculaire électrique et mécanique. Bien que la fatigue musculaire représente une variation dans le temps, une dépendance aux sujets et aux protocoles, la méthode proposée d'identification adaptative, basée sur le filtre de Kalman, est capable de prédire le couple articulaire variant dans le temps de manière systématique. La robustesse de la prédiction du couple articulaire a été évaluée lors d'une tâche de suivi de la fatigue en expérimentation chez des sujets blessés médulaires.Les résultats montrent une bonne performance de suivi des variations d'état des muscles en présence de fatigue et face à d'autres perturbations. Basé sur les performances de précision de la méthode prédictive proposée, une nouvelle stratégie de commande basée sur le retour EMG, «EMG-Feedback Predictive Control» (EFPC), est proposée afin de contrôler de manière adaptative les séquences de stimulation en compensant la variation dans le temps de l'état du muscle. De plus, cette stratégie de commande permet explicitement d'éviter d'appliquer une stimulation excessive aux patients, et de générer les séquences de stimulation appropriées pour obtenir la trajectoire désirée des couples articulaires. / Functional electrical stimulation (FES) has the potential to provide active improvement to spinal cord injured (SCI) patients in terms of mobility, stability and side-effect prevention. In the domain of lower limb FES system, elicited muscle force must be provided appropriately to perform intended movement and the torque generation by FES should be accurate not to lose the posture balance. However, muscle state changes such as muscle fatigue is a major cause which degrades its performance. In addition, most of the complete SCI patients don't have sensory feedback to detect the fatigue and in-vivo joint torque sensor is not available yet. Conventional FES control systems are either in open-loop or not robust to muscle state changes. This thesis aims at a development of joint torque prediction and feedback control in order to enhance the FES performance in terms of accuracy, robustness, and safety to the patients.In order to predict FES-induced joint torque, evoked-Electromyography (eEMG) has been applied to correlate muscle electrical activity and mechanical activity. Although muscle fatigue represents time-variant, subject-specific and protocol-specific characteristics, the proposed Kalman filter-based adaptive identification was able to predict the time-variant torque systematically. The robustness of the torque prediction has been investigated in a fatigue tracking task in experiment with SCI subjects. The results demonstrated good tracking performance for muscle variations and against some disturbances.Based on accurate predictive performance of the proposed method, a new control strategy, EMG-Feedback Predictive Control (EFPC), was proposed to adaptively control stimulation pattern compensating to time-varying muscle state changes. In addition, this control strategy was able to explicitly avoid overstimulation to the patients, and conveniently generate appropriate stimulation pattern for desired torque trajectory.