In this thesis, a passive exoskeleton spine was designed and evaluated by a series of biomechanics simulations. The design objectives were to reduce the human operator’s back muscle efforts and the intervertebral reaction torques during a full range sagittal plane spine flexion/extension. The biomechanics simulations were performed using the OpenSim modeling environment. To manipulate the simulations, a full body musculoskeletal model was created based on the OpenSim gait2354 and “lumbar spine” models. To support flexion and extension of the torso a “push-pull” strategy was proposed by applying external pushing and pulling forces on different locations on the torso. The external forces were optimized via simulations and then a physical exoskeleton prototype was built to evaluate the “push-pull” strategy in vivo. The prototype was tested on three different subjects where the sEMG and inertial data were collected to estimate the muscle force reduction and intervertebral torque reduction. The prototype assisted the users in sagittal plane flexion/extension and reduced the average muscle force and intervertebral reaction torque by an average of 371 N and 29 Nm, respectively.

Modern soldiers are burdened by an increase in body-borne load due to technological advancements related to their armour and equipment. Despite the potential increase in safety from carrying more protective equipment, a heavier load on the soldier might decrease field performance both cognitively and physically. Additionally, an increasing load on military personnel concurrently increases their risk of musculoskeletal injuries. Therefore, there is a necessity for research on the soldier's biomechanical outcomes under different loading conditions. When it comes to biomechanics research, marker-based technology is widely accepted as the gold standard in terms of motion capture. However, recent advancements in markerless motion capture could allow the quick collection of data in various training environments, while avoiding marker errors. In this research project, the Theia3D markerless motion capture system was compared to the marker-based gold standard for application on participants across varying body-borne load conditions. The aim was to estimate lower body joint kinematics, gastrocnemius lateralis and medialis muscle activation patterns, and lower body joint reaction forces from the two motion capture systems. Data were collected on 16 participants for three repetitions of both walking and running under four body-borne load conditions by both motion capture systems simultaneously. Electromyography (EMG) data of lower limb muscles were collected on the right leg and force plates measured ground reaction forces. A complete musculoskeletal analysis was completed in OpenSim using the Rajagopal full-body model and standard workflow: model scaling, inverse kinematics, residual reduction, static optimization, and joint reaction analysis. Estimations of joint kinematics and joint reaction forces were compared between the two systems using Pearson's correlation coefficient, root-mean-square errors, and Bland-Altman limits of agreement. Very strong correlations (r = 0.960 ± 0.038) and acceptable differences (RMSE = 7.8° ± 2.6°) were observed between the kinematics of the marker-based and markerless systems, with some angle biases due to joint centre differences between systems causing an offset. Because the marker-based motion capture system lost line of sight with markers more frequently in the heavier body-borne load conditions, differences generally increased with heavier body-borne loads. Timing of muscle activations of the gastrocnemius lateralis and medialis as estimated from both systems agreed with the ones measured by the EMG sensors. Joint reaction force results also showed a very strong correlation between the systems but the markerless model seemed to overestimate joint reaction forces when compared to results from the marker-based model. Overall, this research highlighted the potential of markerless motion capture to track participants across all body-borne load conditions. However, more work is necessary on the determination of angle bias between the two systems to improve the use of markerless data with OpenSim models.

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A digital twin project is launched by the Integrative Systems Biology (ISB) research team and led by Gunnar Cedersund. The digital twin project is based on biological models of physiological processes, that can interact and be tailored for a specific person. However, the digital twin can currently not analyse movements of a human body. In this master thesis, the aim was to create a useful pipeline that expands the digital twin project with biomechanical modelling of movements, and also visualises the twins by letting the concept take human form. The biomechanical analysis was done in the software OpenSim, where the movements of a motion captured dance were analysed. To generate a simulation of the motion with an acceptable error in a reasonable computation time, a musculoskeletal model was created in OpenSim and scaled to best fit the anthropometry of the dancer. Then, the motion was estimated with an optimised procedure by using the scaled model and the motion capture data. The Root-Mean Squared (RMS) error of the estimated dance with accuracy 10-6 was 2.39 cm. In this thesis, the torque in each joint for the dance motion was estimated. The loads and muscle forces can also be estimated in OpenSim. One useful application is for calculating energy consumption. In order to calculate muscle forces, external forces needs to be measured while recording motion capture. This is something that will be focused on in the future, when continuing with this project. The visualisation of the digital twins were made in Unreal Engine with MetaHuman avatars. The dance recorded in motion capture, were applied to the avatars in order to make them dance. The recorded dance was the same for both OpenSim and Unreal Engine, so the dance could both be viewed and analysed. In conclusion, we have added a new feature to the existing digital twin technology: movements and simulation of the musculoskeletal system. This new feature can in the future be used for both medical purposes such as movement-based rehabilitation as well as for integration into dance performances.

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Robotic exoskeletons are designed to assist patients with motor dysfunctions. Recent researches focus on extending the robotic assistance to patient activities other than ground level walking. This study aims to analyse the lower limb muscle activity during inclined slope walking contrasting with that of ground level walking. Two different angles of inclination were chosen: 9 degrees and 18 degrees. 9 degrees inclined slope is the universal ramp size for wheelchairs. The hypothesis is that muscle activation, and ultimately metabolic cost, in inclined slope walking is different from that of ground level walking. Collected motion data and simulation in OpenSim prove that the difference in metabolic cost is because of increased activity of ankle dorsiflexors and hip extensors and reduced activity of knee extensors. Finally, muscle activities along with other criteria such as kinematic alignment and joint range of motion are summed up as biomechanical considerations for robotic exoskeleton design.

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