The aim of this thesis is to create a low-cost sensor equipped glove using commercially available components that can be used to obtain position, velocity and acceleration data for individual fingers of a hand within an optical motion capture environment. Tracking the full degrees of freedoms of the hand and finger motions without any hindrances is a challenging task in optical motion capture measurements. Attaching markers on every finger and hand joint makes motion capture systems troublesome due to practical problems such as blind spots and/or obtaining higher derivative motion constraints, such as velocities and accelerations. To alleviate this, we propose a method to capture the hand and finger kinematics with a reduced set of optical markers. Additionally inertial sensors are attached to the fingertips to obtain linear acceleration measurements. For optimal velocity estimation, a Kinematic Kalman Filter (KKF) is implemented and its result is compared to the time derivative of the Motion Capture System measurement. The higher derivative specifications are related to contact and curvature constraints between the fingers and the grasped object and are later used in formulating the synthesis task for the design of robotic fingers and hands. A preliminary prototype device has been developed to obtain position, velocity and acceleration information of each fingertip by incorporating multiple accelerometers into the basic design of reduced marker set.

In various martial arts, competitors are interested in quantifying and categorising techniques which are exercised during training. The implementation of embedded systems into training gear, especially a portable wireless body worn system, based on inertial sensors, facilitates the quantification and categorisation of forces and accelerations involved during the training of martial arts. The scope of this paper is to give a brief overview of contemporary technology and devices, describe key methods that are implemented in such devices and as well to provide a project outline. For this reason a literature research was carried out using search engines and databases. The search yielded that devices are capable of tracking activities of daily life; however only one product was found which can track and recognise techniques applied in martial arts training just for the upper body. The realisation of a sensor based tracking system which can track and classify different exercises is possible with present technology. Even so, different challenges such as implementing a sensor fusion and classifier algorithm need to be overcome. Therefore, the project development has to be viewed as an iterative process.

A healthy posture protects the body-supporting functions and prevents injuries by maintaining balance. Literature in performing arts medicine suggests that posture is an important component to prevent piano-playing related injuries. However, no known research studies have quantified, characterized, and compared pianists' sitting postures. The purpose of this study was to explore the relationship between playing postures and perceived pain among pianists. This study applied innovative approach using qualitative and quantitative methods, combined with three-dimensional motion captured technology. To examine risk factors related pianists' postures, three-dimensional motion-capture cameras recorded approximate 40 pianists' postures in various situations; data recordings were combined with a statistical method to investigate pain-posture correlations. Results reveal that the degrees of head-neck or body tilt angles are the tendency of risk factors for piano-playing related pain. Results from this study may have multiple practical implications among which are: (1) a risk factor pain, injury index, or indicator (2) a performance habits profile and (3) practice guide to prevention of piano-playing related musculoskeletal disorders.

In Canada, approximately 544,000 upper-limb injuries occurred in a 12-month period between 2009 and 2010, many of which were injuries to the rotator cuff muscles of the shoulder. Because of the complex structure and function of the shoulder, it is often difficult to determine which muscles have been injured. The most widely used technology to study human movement is motion capture, wherein markers are affixed to a subject’s skin and are tracked by cameras as the subject moves. The recorded marker trajectories are then used to estimate the bone locations and joint angles during the tracked motion. This is called an inverse kinematic simulation. The simulation can then be used to estimate variables that are difficult or impossible to measure directly, such as the activation of single muscle heads within a muscle group. However, muscles bulge and skin stretches during movement, so the markers that are affixed to the skin generally move relative to the underlying bones. These errors, known as soft-tissue artifacts, lead to uncertainty in the calculation of bone locations and, consequently, uncertainty in the computed skeletal joint angles. This uncertainty limits the use of inverse kinematic simulations in clinical settings. Given the skin tissue’s elastic behaviour, a spring-based equilibrium model can be used to estimate the behaviour of skin during non-impulsive motion. In the proposed model, markers were placed on the surface of ellipsoids (representing the thorax, abdomen, scapula, and upper arm) and were attached to each other via springs. The system was assumed to remain in static equilibrium during sufficiently slow movements to approximate the stretch of the skin. In this thesis, the development and application of a proof-of-concept model to estimate the pose of the skeleton is described. This work demonstrates the feasibility of using such a model to reduce errors due to soft-tissue motion.

Body armor designs that limit the range-of-motion required for vital law enforcement tasks, such as shooting may be dangerous. Therefore, a posture based biomechanical analysis was performed to determine if upper body joint angles can be used to assess the effects of armor designs on assumed shooting. Participants (n=8) completed a battery of simulated duty tasks for three armor configurations (no armor, concealable, and tactical armor) while motion capture was used to compute included joint angles of the upper extremity and neck. In general, joint angles were impacted by armor configuration, and law enforcement experience (measured in years) significantly impacted their shooting posture. It was also found that the types of tasks performed interacted with shooting stance. This research is a first step at developing a method to analyze body armor designs and their impact on wearers, so that mobility may not need to be sacrificed for additional protective coverage.

No description available.

Modeling the dynamics of complex multibody systems, such as those representing the motion of animals, can be accomplished through well-established geometric methods. In these formulations, motions take values in certain types of smooth manifolds which are coordinate-free and intrinsic. However, the dimension of the full configuration manifold can be large. The first study in this dissertation aims to build low-dimensional models models from motion capture data. This study also expands on the so-called learning problem from statistical learning theory over Euclidean spaces to estimating functions over manifolds. Experimental results are presented for estimating reptilian motion using motion capture data. The second study in this dissertation utilizes reproducing kernel Hilbert space (RKHS) formulations and Koopman theory, to achieve some of the advantages of learning theory for IID discrete systems to estimates generated over dynamical systems. Specifically, rates of convergence are determined for estimates generated via extended dynamic mode decomposition (EDMD) by relating them to estimates generated by distribution-free learning theory. Some analytical examples illustrate the qualitative behavior of the estimates. Additionally, a examination of the numerical stability of the estimates is also provided in this study. The approximation methods are then implemented to estimate forward kinematics using motion capture data of a human running along a treadmill. The final study of this dissertation contains an examination of the continuous time regression problem over subsets and manifolds. Rates of convergence are determined using a new notion of Persistency of Excitation over flows of manifolds. For practical considerations, two approximation methods of the exact solution to the continuous regression problem are introduced. Characteristics of these approximation methods are analyzed using numerical simulations. Implementations of the approximation schemes are also performed on experimentally collected motion capture data. / Doctor of Philosophy / Modeling the dynamics of complex multibody systems, such as those representing the motion of animals, can be accomplished through well-established geometric methods. However, many real-world systems, including those representing animal motion, are difficult to model from first principles. Machine learning, on the other hand, has proven to be extremely powerful in its ability to leverage "big data" to generate estimates from typically independent and identically distributed (IID) data. This dissertation expands on the so-called learning problem from statistical learning theory over Euclidean spaces to those over manifolds. This dissertation consists of three studies, the first of which aims to build low-dimensional models models from motion capture data. Using the distribution-free learning theory, estimates discussed in this dissertation minimize a proxy of the expected error, which cannot be calculated in closed form. This dissertation also includes a study into approximations of the so-called Koopman operator. This study determined that the rate of convergence of the estimate to the true operator depends on the reduced dimensionality of the embedded submanifold in the high-dimensional ambient input space. While most of the current work on machine learning focuses on cases where the samples used for learning or regression are generated from an IID, stochastic, discrete measurement process, this dissertation also contains a study of the regression problem in continuous time over subsets and manifolds. Additionally, two approximation methods of the exact solution to the continuous regression problem are introduced. Each of the aforementioned studies also includes several analytical results to illustrate the qualitative behavior of the approximations and, in each study, implementations of the estimation schemes are performed on experimentally collected motion capture data.

Accurate risk assessment tools and methods are necessary to understand the relationship between occupational exposure to physical risk factors and musculoskeletal disorders. Ergonomists typically consider direct measurement methods to be the most objective and accurate of the available tools. However, direct measurement methods are often not used due to cost, practicality, and worker/workplace disruption. Inertial measurement units (IMUs), a relatively new direct measurement technology used to assess worker kinematics, are attractive to ergonomists due to their small size, low cost, and ability to reliably capture information across full working shifts. IMUs are often touted as a field-capable alternative to optical motion capture systems (OMCs). The error magnitudes of IMUs, however, can vary significantly (>15°) both within and across studies. The overall goals of this thesis were to (i) provide knowledge about the capabilities and limitations of IMUs in order to explain the inconsistencies observed in previous studies that assessed IMU accuracy, and (ii) provide guidance for the ergonomics community to leverage this technology. All three studies in this dissertation systematically evaluated IMUs using a repetitive material transfer task performed by thirteen participants with varying movement speeds (15, 30, 45 cycles/minute) and magnetic disturbance (absent, present). An OMC system was used as the reference device. This first study systematically evaluated the effects of motion speed and magnetic disturbance on the spatial orientation accuracy of an inertial measurement unit (IMU) worn on the hand. Root-mean-square differences (RMSD) exceeded 20° when inclination measurements (pitch and roll) were calculated using the IMU’s accelerometer. A linear Kalman filter and a proprietary, embedded Kalman filter reduced inclination RMSD to < 3° across all movement speeds. The RMSD in the heading direction (i.e., about gravity) increased (from < 5° to 17°) under magnetic disturbance. The linear Kalman filter and the embedded Kalman filter reduced heading RMSD to < 12° and < 7°, respectively. This study indicated that the use of IMUs and Kalman filters can improve inclinometer measurement accuracy. However, magnetic disturbances continue to limit the accuracy of three-dimensional IMU motion capture. The goal of the second study was to understand the capability of IMU inclinometers to improve estimates of angular displacements and velocities of the upper arm. RMSD and peak displacement error exceeded 11° and 28° at the fastest transfer rate (45 cycles/min) when upper arm elevation was calculated using the IMU accelerometer. The implementation of a Kalman filter reduced RMS and peak errors to < 1.5° and < 2.3°, respectively. Similarly, the RMS and peak error for accelerometer-derived velocities exceeded 81°/s and 221.3°/s, respectively, at the fastest transfer rate. The Kalman filter reduced RMS and peak errors to < 9.2°/s and < 25.1°/s, respectively. The third study was conducted to evaluate the relationship between magnetic field strength variation and magnetic heading deviation. In this study, the presence of the metal plate increased magnetic heading deviations from < 12° (90th-10th percentile) to approximately 30°. As expected, the magnetic field strength standard deviation increased from 1.0uT to 2.4uT. While this relationship may differ across other sources of magnetic disturbance, the results reinforce the notion that local magnetic field disturbances should be minimized when using IMUs for human motion capture. Overall, the findings from this thesis contribute to the ergonomics community’s understanding of the current capabilities and limitations of IMUs. These studies suggest that while the touted capabilities of the IMUs (full-body motion capture in workplace settings) may be unattainable based on current sensor technology, these sensors are still significantly more accurate than the accelerometer-based inclinometers commonly used by ergonomists to measure motions of the upper arms.

This thesis presents a study on how to create a production pipeline using a markerless motion capture system for the creation of animations in computer games. The questions the authors desire to answer are: Is it possible to create a pipeline that uses markerless motion capture for the creation of animations in computer games? And also: Can a markerless motion capture system fit in an animation pipeline for games? This thesis is based on previous work by Kakee Lau (Lau, 2012), a former student of Gotland University College. He describes a pipeline for working with passive optical motion capture for games. To fit the markerless motion capture system, there must be some changes to Lau’s already established pipeline. The method used in this thesis is based on a pipeline described in Lau’s thesis (Lau, 2012). The authors have made some alterations to this pipeline for it to be more suitable for markerless motion capture. The pipeline that the authors propose covers the setup of two Kinect cameras, the calibration, the recording, the cleaning and the preparation for MotionBuilder. Due to some factors that were not taken into consideration during testing, there cannot be any quantitative conclusion in this thesis to which system is the better one. Based on the findings of this study the authors can conclude that a markerless motion capture system is a viable method for game animation creation, yet not giving the same quality of results as a passive optical motion capture system.

Hårdvara kan bli gammal, program kan sluta utvecklas, filer som skapats från sådan hårdvara respektive mjukvara kan bli oanvändbara med tiden. Samt att hålla ordning på många individuella filer kan i längden bli jobbigt för användare. Med en databasorienterad lagrinsgslösning kan olika API:er användas för att göra data kompatibel med flera olika verktyg och program, samt att det kan användas för att skapa en centraliserad lösning för att enkelt hålla ordning på information. Bland databaser finns det två primära grupperingar: SQL och NoSQL. Detta arbete ämnar undersöka vilken typ som passar för att hantera motion capture data. Tester har utförts på SQLs MySQL och NoSQLs Neo4j. Neo4j som är specialiserad för att hantera data som motion capture data. Resultatet från testningarna är förvånande nog att MySQL hanterar motion capture data bättre än Neo4j. Ytterligare arbeten för att undersöka fler varianter av databaser för en mer komplett bild föreslås.