The purpose of this paper is to investigate the possibility of replacing data from highspeed filming (Qualisys motion capture) with data from Inertial Measurement Units (X-io technologies), when used to run a model of torso and pelvis in OpenSim. Qualisys motion capture data is used as the golden standard to validate the result visually and with Bland-Altman plots. In order to obtain comparable data experiments are conducted where both methods of collecting data are used simultaneously. Data from the IMU's then need to be processed in Matlab before it can be used to run the OpenSim modell. Several Matlab programs rotate the IMU data to a static reference frame, filter and integrate it, then create viritual markers that correspond to Qualisys' optical markers. The conclusion is that using IMU as a method for collecting data can replace Qualisys in some applications, but not in ones that require high precision. However, this paper only begins the examination of IMU's and there are most likely improvements to be made.

As the robotic industry has matured, the study of animal motion has given rise to many robot designs. Researchers from multiple areas, such as biomechanics, control theory, and machine learning, have spent their energy and efforts making robots more realistic. The intent is that the automatic system can replace real animals and even perform certain tasks in harsh, or even dangerous environments. However, animal motions encompass a wide range of motion that depends on body geometries and various animal behaviors. From human walking to lizards crawling, from dogs running to horses pacing, many studies of motion only focus on one species or a few behaviors. An ever-increasing collection of papers are published that study animal motions for different species and motion regimes, and these are often based on video footage and motion capture data. This is particularly true for human motion research. While there are huge volumes of data acquired from motion capture and video, not many researches as of yet are using dynamical system analysis techniques such as dynamic mode decomposition, extended dynamic mode decomposition, or even Koopman method to understand and compare the motion across different species. Thus, the goal of this thesis is to further develop the methods mentioned above to analyze and characterize animal motion. The algorithms derived should apply regardless the shape of the body or the number of degrees of freedom for the joins. Using strategies from statistical learning theory and Koopman operator theory, several methods are derived and compared. The analysis culminates in a motion approximation, that subsequently could be used in robotic control to emulate an animal motion as much as possible. / Master of Science / As the robotic industry has matured, the study of animal motion has given rise to many robot designs. Researchers from multiple areas, such as biomechanics, control theory, and machine learning, devote their energy and efforts to making the robots more realistic, so that the automatic system can replace real animals and even perform certain tasks in harsher, or even dangerous environment. However, animal motion itself encompasses a wide range of motions that depend on body geometries and various behaviors. From human walking to lizards crawling, from dogs running to horses pacing, many studies of motion only focus on one species or a few behaviors. Many animal motion studies are often based on video footage and motion capture data. This is particularly true for human motion research when researchers are trying to create the gait pattern in medical research. While there are huge volumes of data acquired from motion captures and videos, not many researches as of yet are using dynamical system analysis techniques to understand and compare the motion across different species. Thus, the goal of this thesis is to use dynamical system analysis techniques and further develop the methods to analyze and characterize animal motion. Regardless of the shape of the body or the join types at different locations on the body, strategies from the theories of machine learning and dynamical system analysis are used to derive algorithms which should be applied to all animal motions. Several methods are derived and compared. The analysis culminates in a motion approximation, that subsequently could be used in robotic control to emulate an animal motion as realistic as possible.

In this thesis I will propose and test a pipeline for creating in-game animations using motion capture technology, with the aim to export to a 3D game engine, such as UDK or similar. Even though there are already established pipelines for creating in-game animations in the game industry, they are not of public knowledge and dissemination of information is hindered by the severe NDA (non-disclosure agreements) imposed on the studios and artists. I will therefore use my experience with game production and as a student, to research and propose an optimal pipeline for developing in-game animation that may be used by other students, researchers and independent game developers. The methodology used to test the pipeline will be a case study based on the student project Synergy, which I am developing with other third year students also attending the Game design Education at Gotland University.

No description available.

Slips are a leading cause of injury among older adults. Specific slip recovery measures, including slip distance and peak slip speed, have been shown to increase significantly among fallers as compared to non-fallers. Often, slipping kinematics are measured using optoelectronic motion capture (OMC), requiring a laboratory setting and limiting data collection to experimentally-controlled conditions. Inertial measurement units (IMUs) show promise as a portable and wearable form of motion capture. This study had two objectives. First, we investigated whether foot and ankle IMU-derived slip recovery measures could be considered equivalent to the same OMC-derived measures. Second, we investigated if both participant-placed and researcher-placed IMU-derived slip recovery measures could be considered equivalent to the same OMC-derived measures. 30 older adults (ages 65-80) were exposed to a slip while wearing both IMUs and OMC markers. Slip distance and peak slip speed were measured by both systems and compared. Equivalence testing (α = 0.05) showed that IMUs placed on the foot and the ankle were equivalent to OMC in measuring these slip recovery measures. Furthermore, it was shown that researcher and participant-placed IMUs were equivalent (α = 0.05) to OMC in measuring these slip recovery measures. These results confirm that IMUs can be a viable substitute for OMC and have the potential to expand data capture to a real-world environment. / Master of Science / Falls are a major cause of injury among older adults. Slips are a large contributor to falls, so it is important to better understand how slips occur to develop more efficient fall-prevention programs. To understand slips, previous research often utilized optoelectronic motion capture (OMC) to measure slip recovery measures. However, OMC relies on multiple cameras, limiting slip measurement to a laboratory. As we want to understand slips in the real-world, we must use a different form of motion capture. Inertial measurement units (IMUs) are sensors that can afford real-world biomechanical measurement. In this thesis research, 30 older adults (ages 65-80) were exposed to one over-ground slip. Slip recovery measures are compared between OMC and IMUs placed on the foot and ankle. Furthermore, IMU placement is compared between researchers and participants. Equivalence testing showed that IMUs placed on the foot and the ankle were equivalent to OMC in measuring these slip recovery measures. Furthermore, it was shown that researcher and participant-placed IMUs were equivalent to OMC in measuring these slip recovery measures. These results confirm that IMUs can be a viable substitute for OMC and have the potential to expand data capture to a real-world environment.

We present a solution to animating interactions between characters and thin, non-rigid bodies using a passive optical motion capture system. Prior work in human body motion capture can accurately recreate human motion but this work is not adequate because it does not allow for interactions with a non-rigid body. Prior work in face and cloth motion capture handles non-rigid planes but rope is better handled with a curved spline rather than a curved plane. The segmented motion is in the form of un-indexed motion capture data. After segmenting the motion of the thin, non-rigid body and the human character the separated motion capture data can be recreated individually. The recreated motion streams are then recombined using 3D modeling and animation software. The presented solution also improves techniques for recreating thin, non-rigid body motion from un-indexed motion capture data. Using the linear combination of two predicted marker positions our solution can accurately track motion capture markers through each frame of the motion capture data. This also allows our solution to invent marker positions when gaps are present in the motion capture data. Our improvements allow users to reconstruct the motion of both a human character and a thin, non-rigid body simultaneously from motion capture data gathered in a mixed motion capture session.

This thesis is concerned with the solutions to several issues, including the problems of joint localisation, motion de-noising/smoothing, and soft tissue artefacts correction, in skeletal motion reconstruction for motion analysis, using marker-based optical motion capture technologies. We propose a very efficient joint localisation method, which only needs to optimise over three parameters, regardless of the total numbers of markers and frames. A framework powered by this joint localisation solution is also developed, which can automatically find all the joints in an articulated body structure, and significantly reduce the total number of markers needed in a typical motion capture session, by implementing a solvability propagation process. This framework is also configured to operate in a hybrid scheme, which can automatically switch between the primary joint estimator and a slower solution having fewer conditions regarding the required number of markers on a given body segment. This makes the framework workable even for extreme scenarios in which there are fewer than three markers on any body segment. A non-linear optimisation method for 3D trajectory smoothing is also proposed for de-noising the estimated joint paths. By immobilising a series of characteristic points in the trajectory, this method is able to effectively preserve detailed information for vigorous motion sequences. Various other smoothing techniques in the literature are also discussed and compared, concluding that a size-3 weighted average filter implemented in an automatic manner is a good real-time solution for low intensity activities. The effects of skin deformation on marker position data, known as soft tissue artefacts, are learned via a behavioural study on the human upper-body, with specific emphasis on combined limb actions. Based on the experimental findings, mathematical models are proposed to characterise the development of different types of artefacts, including translational, rotational, and transverse. We also theoretically demonstrate the feasibility of using a Kalman filter to correct the soft tissue artefacts, using the mathematical models.

This research examines an approach for capturing 3D surface and structural data of moving articulated creatures. Given the task of non-invasively and automatically capturing such data, a methodology and the associated experiments are presented, that apply to multiview videos of the subjects motion. Our thesis states: A functional structure and the timevarying surface of an articulated creature subject are contained in a sequence of its 3D data. A functional structure is one example of the possible arrangements of internal mechanisms (kinematic joints, springs, etc.) that is capable of performing the motions observed in the input data. Volumetric structures are frequently used as shape descriptors for 3D data. The capture of such data is being facilitated by developments in multi-view video and range scanning, extending to subjects that are alive and moving. In this research, we examine vision-based modeling and the related representation of moving articulated creatures using Spines. We define a Spine as a branching axial structure representing the shape and topology of a 3D objects limbs, and capturing the limbs correspondence and motion over time. The Spine concept builds on skeletal representations often used to describe the internal structure of an articulated object and the significant protrusions. Our representation of a Spine provides for enhancements over a 3D skeleton. These enhancements form temporally consistent limb hierarchies that contain correspondence information about real motion data. We present a practical implementation that approximates a Spines joint probability function to reconstruct Spines for synthetic and real subjects that move. In general, our approach combines the objectives of generalized cylinders, 3D scanning, and markerless motion capture to generate baseline models from real puppets, animals, and human subjects.

Research in robotics has recently broadened its traditional focus on industrial applications to include natural, human-like systems. The human musculoskeletal system has over 600 muscles and 200 joint degrees-of-freedom that provide extraordinary flexibility in tailoring its overall configuration and dynamics to the demands of different tasks. The importance of understanding human movement has spurred efforts to build systems with similar capabilities and has led to the construction of actuators, such as pneumatic artificial muscles, that have properties similar to those of human muscles. However, muscles are far more complex than these robotic actuators and will require new control perspectives. Specifying how to encode high degree-of-freedom muscle functions in order to recreate such movements in anthropomorphic robotic systems is an imposing challenge. This dissertation attempts to advance our understanding by modeling the workings of human muscles in a way that explains how the low temporal bandwidth control of the human brain could direct the high temporal bandwidth requirements of the human movement system. We extend the motor primitives model, a popular strategy for human motor control, by coding a fixed library of movements such that their temporal codes are pre-computed and can be looked up and combined on demand. In this dissertation we develop primitives that lead to various smooth, natural human movements and obtain a sparse-code representation for muscle fiber length changes by applying Matching Pursuit on a parameterized representation of such movements. We employ accurate three-dimensional musculoskeletal models to simulate the lower body muscle fiber length changes for multiple repeatable movements captured from human subjects. We recreate the length changes and show that the signal can be economically encoded in terms of discrete movement elements. Each movement can thus be visualized as a sequence of coefficients for temporally displaced motor primitives. The primary research contribution of describing movements as a compact code develops a clear hierarchy between the spinal cord and higher brain areas. The code has several other advantages. First, it provides an overview of how the elaborate computations in abstract motor control could be ‘parcellated’ into the brain’s primary subsystems. Second, its parametric description could be used in the extension of learned movements to similar movements with different goals. Thirdly, the sensitivity of the parameters can allow the differentiation of very subtle variations in movement. This research lays the groundwork for understanding and developing further human motor control strategies and provides a mathematical framework for experimental research. / text

Det här arbetet syftade till att undersöka om de skakningar och ryck som uppstår vid en dålig motion capture-inspelning, kan användas till fördel i skräcksammanhang och utveckla den skrämmande egenskapen hos en zombiekaraktär. Som bakgrund har undersökningen gått igenom litteratur kring Zombies, The uncanny valley, Das Unheimliche och deras förhållande till spel.Till det material som skapades till undersökningen genomfördes en motion capture-inspelning och sedan skapades tre stycken videoklipp föreställande en zombie. Datan ur inspelningen redigerades på tre olika sätt och varierade mängden ryckighet i zombiens rörelser. Undersökningen utfördes med intervjuer av både icke-skräckerfarana och skräckerfarna informanter.Resultatet visade att det klipp där skak och ryck tillförts uppfattades som mest läskigast av en majoritet av informanterna. Slutsatsen skulle däremot ha kunnat validerats i högre utsträckning med fler intervjuade informanter.