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A control method is proposed for exercising specific muscles of a human's lower body. This is accomplished using an exoskeleton that imposes active force feedback control. The proposed method involves a combined dynamic model of the musculoskeletal system of the lower-body with the dynamics of pneumatic actuators. The exoskeleton is designed to allow for individual control of mono-articular or bi-articular muscles to be exercised while not inhibiting the subject's range of motion. The control method has been implemented in a 1-Degree of Freedom (DOF) exoskeleton that is designed to resist the motion of the human knee by applying actuator forces in opposition to a specified muscle force profile. In this research, there is a discussion on the model of the human's lower body and how muscles are affected as a function of joint positions. Then it is discussed how to calculate for the forces needed by a pneumatic actuator to oppose the muscles to create the desired muscle force profile at a given joint angles. The proposed exoskeleton could be utilized either for rehabilitation purposes, to prevent muscle atrophy and bone loss of astronauts, or for muscle training in general.

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Study Design: Computational analyses using optimization finite element (FE) models. Objective: To determine the spinal muscle forces (MFs) creating compressive follower loads (CFL) in the lumbar spine in various sagittal postures and to investigate if such MFs can maintain the spinal stability. Summary of Background Data: Biomechanical loads are known closely associated with spinal disorders. Normal spinal loads, however, remains poorly understood due to the lack of knowledge of the MF control mechanism for normal biomechanical functions. Methods: 3-D optimization and FE models of the spinal system (trunk, lumbar spine, sacrum, pelvis, and 232 muscles) were developed and validated using reported experimental data. Optimization models were used to determine the MFs creating CFLs in the lumbar spine in various sagittal postures from 10 extension to 40 flexion. The deformation of the lumbar spine under these MFs and trunk weight was predicted from FE models. The stable lumbar spine deformation was determined by the resultant trunk sway < 10 mm. Results: Optimization solutions of MFs, CFLs, and follower load path (FLP) location were feasible for all studied postures. The FE predictions clearly demonstrated that MFs creating CFLs along the base spinal curve connecting the geometrical centers or along a curve in its vicinity (within anterior or posterior shift by 2 mm) produce the stable deformation of the lumbar spine in the neutral standing and flexed postures, whereas the MFs creating the smallest CFLs resulted in the unstable deformation. In case of extended postures, however, it was not possible to find the CFL creating MFs that produce stable deformation of the extended spine. Conclusion: The results of this study demonstrated the feasibility for spinal muscles to stabilize the spine via the CFL mechanism.

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Αντικείμενο της παρούσας διπλωματικής εργασίας είναι αρχικά η καταγραφή της ανθρώπινης κίνησης με κάποια συσκευή παρακολούθησης και κατόπιν η δημιουργία ενός αντιπροσωπευτικού μοντέλου, ώστε να μπορεί να μελετηθεί η δυναμική του συμπεριφορά. Ως συσκευή καταγραφής χρησιμοποιήθηκε ο αισθητήρας Kinect της Microsoft. Το μοντέλο που αναπτύχθηκε αφορά κυρίως τα κάτω άκρα του ανθρώπου και επιπλέον διαθέτει μυοσκελετική δομή με 86 μύες. Στα πλαίσια των αναλύσεων χρησιμοποιήθηκαν διάφορες τεχνικές για την εξαγωγή των αποτελεσμάτων, όπως είναι η αντίστροφη κινηματική, αντίστροφη δυναμική, υπολογισμός μυϊκών διεγέρσεων και ορθή δυναμική και προτείνουμε μια στρατηγική για την ανάλυση και την εξαγωγή αποτελεσμάτων. / The research developed in this thesis first deal with the problem of capturing the human body motion and then concentrates on the creation of musculoskeletal models, which can capture and accurately study its dynamical behavior. The Microsoft's Kinect sensor was utilized to capture the human motion. The model used for the simulations is the human lower extremity with 86 attached muscles. For the analysis phase we used some common methods such as inverse kinematics, inverse dynamics, computed muscle control and forward dynamics and we showed a general pipeline strategy for generating correct results.

Control of systems requiring direct physical human-robot interaction (pHRI) requires special consideration of the motion, dynamics, and control of both the human and the robot. Humans actively change their dynamic characteristics during motion, and robots should be designed with this in mind. Both the case of humans trying to control haptic robots using physical contact and the case of using wearable robots that must work with human muscles are pHRI systems. Force feedback haptic devices require physical contact between the operator and the machine, which creates a coupled system. This human contact creates a situation in which the stiffness of the system changes based on how the operator modulates the stiffness of their arm. The natural human tendency is to increase arm stiffness to attempt to stabilize motion. However, this increases the overall stiffness of the system, making it more difficult to control and reducing stability. Instability poses a threat of injury or load damage for large assistive haptic devices with heavy loads. Controllers do not typically account for this, as operator stiffness is often not directly measurable. The common solution of using a controller with significantly increased controller damping has the disadvantage of slowing the device and decreasing operator efficiency. By expanding the information available to the controller, it can be designed to adjust a robot's motion based on the how the operator is interacting with it and allow for faster movement in low stiffness situations. This research explored the utility of a system that can estimate operator arm stiffness and compensate accordingly. By measuring muscle activity, a model of the human arm was utilized to estimate the stiffness level of the operator, and then adjust the gains of an impedance-based controller to stabilize the device. This achieved the goal of reducing oscillations and increasing device performance, as demonstrated through a series of user trials with the device. Through the design of this system, the effectiveness of a variety of operator models were analyzed and several different controllers were explored. The final device has the potential to increase the performance of operators and reduce fatigue due to usage, which in industrial settings could translate into better efficiency and higher productivity. Similarly, wearable robots must consider human muscle activity. Wearable robots, often called exoskeleton robots, are used for a variety of tasks, including force amplification, rehabilitation, and medical diagnosis. Force amplification exoskeletons operate much like haptic assist devices, and could leverage the same adaptive control system. The latter two types, however, are designed with the purpose of modulating human muscles, in which case the wearer's muscles must adapt to the way the robot moves, the reverse of the robot adapting to how the human moves. In this case, the robot controller must apply a force to the arm to cause the arm muscles to adapt and generate a specific muscle activity pattern. This related problem is explored and a muscle control algorithm is designed that allows a wearable robot to induce a specified muscle pattern in the wearer's arm. The two problems, in which the robot must adapt to the human's motion and in which the robot must induce the human to adapt its motion, are related critical problems that must be solved to enable simple and natural physical human robot interaction.