Joint kinematics of upper extremity (UE) impairments in a pediatric population are often difficult to examine using marker-based motion capture. As a result of the cost and availability of tools such as marker-based motion capture in clinical settings, clinicians use functional tasks to examine improvement in movement quality. However, some of these tasks, such as the Box and Block test (BBT), which is examined in this study, rely on scoring to assess motor improvement. This scoring method can be misleading due to the possibility of movement compensation to improve scores. Therefore, finding kinematic correlations that can lead to improved BBT scores could improve the quality of functional assessments by providing discrete measures for clinicians. Understanding human motion using marker-based motion capture has been the accepted standard in biomechanics. However, it is not without its drawbacks, especially in upper extremity examination due to complex anatomical positioning. The introduction of markerless motion capture software could drastically alter how human biomechanics is analyzed in various settings. Additionally, avoiding possible errors due to clothing and skin movement could greatly improve reported results. Therefore, examining similarities in UE joint kinematics between accepted marker-based and markerless software could introduce markerless motion capture as a method for examining complex kinematics. This study aims to examine UE joint kinematics in a typically developing pediatric population while they complete the BBT, as well as validate Theia3D (Theia Markerless Inc., Kingston, ON, Canada). Marker-based motion capture was used to capture UE kinematics during the BBT. This study was performed on typically developing children aged 7, 9, and 11. Average and peak joint angles were determined, as well as hand segment velocity and path length. Significant correlations to BBT scores were found in peak shoulder flexion (FLEX) angle (r = -0.556, p-value = 0.009), peak (r = -0.479, p-value = 0.028), and average (ρ = -0.535, p-value = 0.012) wrist extension (EXT) angle, average mediolateral (ML) hand segment velocity (r = 0.494, p-value = 0.023), and path length (r = -0.522, p-value = 0.015). Additionally, significant differences between BBT scores (p-value = 0.005), peak shoulder FLEX (p-value = 0.024), and peak shoulder abduction (ABD) angle (p-value = 0.022) were found between the 7- and 11-year-old age groups. Peak elbow FLEX angle was significantly different (p-value = 0.049) between 9- and 11-year-old age groups. These results show that the BBT score could be related to the shoulder and wrist angle, as well as hand segment velocity and path length for typically developing children. Furthermore, root mean square deviation (RMSD) values less than 6° existed in all joint angles. Intraclass correlation coefficients (ICCs) greater than 0.75 were found in shoulder ABD (ICC = 0.79), forearm pronation (ICC = 0.81), wrist EXT (ICC = 0.75), and radial deviation (ICC = 0.87). Additionally, validation results between the marker-based and markerless systems show that there are differences in pose estimations and joint calculations based on rotation sequences. Overall, UE joint kinematics are shown to have correlations to BBT scores, so scores alone may not be indicative of movement quality in other patient populations. Markerless motion capture shows many benefits, however, it should be noted that, due to the complexity of upper extremity motion analysis, understanding what joint rotation sequences align the best with task-specific motions is important. / Master of Science / Human motion is commonly analyzed using marker-based motion capture, which consists of fitting participants with retroreflective markers that can be seen by specialized cameras. However, due to equipment costs, difficult implementation, and the occurrence of markers shifting on skin or being concealed by clothing, markerless motion capture is beginning to be introduced into biomechanics research and could be used in hospitals, clinical settings, and for outdoor examination due to its versatility. The software uses machine learning software that can determine skin landmarks in videos from several cameras to develop a 3D skeleton. Markerless motion capture could be beneficial in examining patients with neuromotor disorders or injuries due to being able to capture abnormal or quick movement which often accompanies many neurological disorders that affect motor function. Additionally, observing movement in children is a challenge due to markers being too close together on smaller limbs. Due to cost and obtainability, clinicians tend to use functional tests to examine improvements in motor function by a scoring system relevant to the specific test, such as the Box and Block test (BBT) which will be used in this study. However, there is the possibility of the patient's ability to adapt to the test to improve their score without improving general motor function. Therefore, it is important to find a relationship between upper limb movement and BBT scores. This study aims to find correlations between upper limb movement and Box and Block test scores as well as differences between 7-, 9-, and 11-year-old age groups and compare marker-based motion capture and the Theia3D (Theia Markerless Inc., Kingston, ON, Canada) markerless motion capture software. Joint assessment is completed with motion capture, which uses reflective markers on specific landmarks on the skin surface. Markerless motion capture is collected simultaneously with marker-based motion capture to assess similarities. The entire procedure was also completed 2 times within 1 visit. The results showed meaningful comparisons between the BBT scores and shoulder and wrist angle, and hand velocity. BBT scores and shoulder angles were shown to be different between the 7- and 11-year-old age groups. Elbow angles were shown to be different between the 9- and 11-year-old age groups. Additionally, comparisons between the marker-based and markerless results showed that all resulting joint angle data captured by each system were similar. Markerless measurement comparisons showed similarities between both sessions as well. These results show that there are ways to provide discrete measurements in clinical settings to examine movement quality. Comparisons between both motion analysis systems show the need to determine task-specific analyses to obtain meaningful results concerning the upper limbs, due to the inherent joint complexity and differing methods of completing the same task.

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Background: Lower limb amputees face many challenges, and most of them prefer to use prosthetics for daily tasks and activities. The prosthesis is usually a combination of connected prosthetic components, and their spatial orientation is called the prosthetic alignment. Proper alignment is essential, as it substantially affects the quality and comfort of a prosthesis.   Objective: The aim of this study was to create a method that could accurately and effectively quantify the alignment of a transtibial and transfemoral prostheses using Vicon optical motion capture system.   Methods: Two experimental series were conducted. The first one was to test the repeatability of the measurement. Three analysts placed retroreflective markers on the prostheses three times, and five measurements were recorded each time. Alignment parameters were calculated in Vicon ProCalc for each measurement, and a standard error of measurement was found for each alignment parameter. The standard error of measurement was calculated from three variance components, between-analyst, within-analyst, and between-trial variability. The second experimental series was conducted to understand the relationship between alignment adjustments and the outcome parameters. The socket height, internal rotation, flexion, adduction, and translation were modified and measured. The socket translation was calculated in three coordinate systems to study how they affect the outcome.   Results: For the first experimental series, the standard error of measurement for every alignment parameter was below 3° and 6 mm. The between-analyst variability was the most prominent, and the parameters calculated in the sagittal plane were more reliable than those calculated in the frontal and transverse plane. In the second experimental series, there was a linear relationship between the modifications and the measured outcome. When a connection between two prosthetic components was changed by turning the screws one round, the average change in angle between them was 2°, and the average translation change was 4.4 mm. Of the three coordinate systems, the translation calculated in ankle coordinates was more reliable than in global coordinates and describe the translation more effectively than in socket coordinates.   Conclusion: The reliability of the measurements was considered good. The standard error of measurement was low, and the main variability resulted from differences in marker placement between the analysts. The results from the measured alignment changes were as expected. All the parameters could be effectively interpreted, and the ankle coordinates were considered advantageous in describing the socket translation.

This thesis presents a method for estimating the positions of human limbs during motion that can be applied to wearable, textile-based sensors. The method was validated for the elbow and shoulder joints with data from two garments with resistive, thread-based sensors sewn into the garments at multiple locations. The proposed method was able to estimate the elbow joint position with an average error of 2.2 degrees. The method also produced an average difference in Euclidean distance of 3.7 degrees for the estimated shoulder joint position using data from nine sensors placed around the subject's shoulder. The most accurate combination of sensors on the shoulder garment was found to produce an average difference in distance of 3.4 degrees and used only six sensors. The characteristics of the resistive, thread-based sensor used to validate the method are also detailed as some of their behaviors proved to affect the accuracy of the method negatively. / Master of Science / Human motion capture systems gather data on the position of the human body during motion. The data is then used to recreate and analyze the motion digitally. There is a need for motion capture devices capable of measuring long-term data on human motion, especially in physical therapy. However, the currently available motion capture systems have limitations that make long-term or daily use either impossible or uncomfortable. This thesis presents a method that uses data from wearable, textile-based sensors to estimate the positions of human limbs during motion. Two garments were used to validate the method on the elbow and shoulder joints. The proposed method was able to measure the elbow and shoulder joints with an average accuracy that is within the acceptable range for clinical settings.

"3D Digitized Romanesque Ermita in Virtual Reality" is a digital exploration of the historical, architectural, and cultural significance of a medieval Romanesque church in Valladolid, Spain. In this project, I recreate Iglesia de Nuestra Señora de la Anunciada, a reconstructed Catholic monastery. A digital replica of the church, produced by photogrammetry in the summer of 2014, has been created in a virtual environment and is experienced through immersive technologies. The use of a head-mounted display is a wearable device in which users view environments three dimensionally, while a Qualisys motion tracking system allows users to physically walk through the virtual world. Additionally, this project explores the potential for the use of virtual reality as a learning tool in classroom and museum settings. / Master of Fine Arts

Human motion collected in real-world environments without instruction from researchers - or natural motion - is an understudied area of the field of motion capture that could increase the efficacy of assistive devices such as exoskeletons, robotics, and prosthetics. With this goal in mind, a natural motion dataset is presented in this thesis alongside algorithms for analyzing human motion. The dataset contains more than 36 hours of inertial motion capture data collected while the 16 participants went about their lives. The participants were not instructed on what actions to perform and interacted freely with real-world environments such as a home improvement store and a college campus. We apply our dataset in two experiments. The first is a study into how manual material handlers lift and bend at work, and what postures they tend to use and why. Workers rarely used symmetric squats and infrequently used symmetric stoops typically studied in lab settings. Instead, they used a variety of different postures that have not been well-characterized such as one-legged lifting and split-legged lifting. The second experiment is a study of how to infer human motion using limited information. We present methods for inferring human motion from sparse sensors using Transformers and Seq2Seq models. We found that Transformers perform better than Seq2Seq models in producing upper-body and full-body motion, but that each model can accurately infer human motion for a variety of postures like sitting, standing, kneeling, and bending given sparse sensor data. / Master of Science / To better design technology that can assist people in their daily lives, it is necessary to better understand how people move and act in the real-world with little to no instruction from researchers. Personal assistants such as Alexa and Google Assistant have benefited from what researchers call natural language processing. Similarly, natural motion processing will be useful for everyday assistive devices like prosthetics and exoskeletons. Unscripted human motion in real-world environments - or natural motion - has been made possible with recent advancements in motion capture technology. In this thesis, we present data from 16 participants who wore a suit that captures accurate human motion. The dataset contains more than 36 hours of unscripted human motion data in real-world environments that is usable by other researchers to develop technology and advance our understanding of human motion. In addition, we perform two experiments in this thesis. The first is a study into how manual material handlers lift and bend at work, and what postures they tend to use and why. The second is a study into how we can determine what a person's body is doing with a limited amount of information from only a few sensors. This study could be useful for making commercial devices like smartphones, smartwatches, and smartglasses more valuable and useful.

Wearable computers and e-textiles are becoming increasingly widespread in today's society. Motion capture is one of the many potential applications for on-body electronic systems. Previous work has been performed at Virginia Tech's E-textiles Laboratory to design a framework for a self-contained loose fit motion capture system. This system gathers information from sensors distributed throughout the body on a "smart" garment. This thesis presents the hardware and software components of the framework, along with improvements made to it. This thesis also presents an analysis of both the on-body and off-body network communication to determine how many sensors can be supported on the garment at a given time. Finally, this thesis presents a method for determining the accuracy of the smart garment and shows how it compares against a commercially available motion capture system. / Master of Science

INTRODUCTION: ACL-reconstructed (ACL-R) patients exhibit side-to-side asymmetries in movement and loading patterns after surgery, some of which are predictive of a secondary ACL injury. These asymmetries have not been fully assessed in healthy athletes. PURPOSE: To quantify side-to-side symmetry in secondary injury predictors in healthy athletes and compare these metrics to those measured in previous cohorts of ACL-R patients, as well as to assess differences in these metrics between two landing tasks and between sexes. METHODS: 60 healthy recreational athletes performed seven trials of a stop-jump task and seven trials of a single-leg hop for distance on each limb. The kinematics and kinetics of the first landing of the stop-jump and the landing of the single-leg hop were analyzed with a 10-camera motion analysis system (240Hz) and 2 embedded force plates (1920Hz). Limb symmetry indices (LSIs) were calculated for each variable and compared between subject groups, tasks, and sexes with Wilcoxon Signed Rank tests (p<0.05). RESULTS: Control subjects exhibited asymmetry in hop distance (p=0.006). ACL-R subjects displayed greater asymmetry in knee flexion variables, peak forces, and peak knee extension moments during the bilateral landing (p<0.001) and in hop distance (p<0.001). Control subjects showed greater asymmetry in knee flexion variables during the single-leg hop (p<0.001). Males and females showed similar symmetry in both tasks. CONCLUSIONS: Symmetry cannot be assumed in control subjects in all metrics. Asymmetries are more prevalent in ACL-R athletes than in healthy controls. Future work will continue to examine the usefulness of each metric in assessing ACL-R rehabilitation. / Master of Science / Up to 200,000 ACL injuries occur in the US annually. Researchers have demonstrated that ACL-reconstructed (ACL-R) patients display differences in movements between their injured leg and their healthy leg during athletic activities. In some cases, these differences, or asymmetries, can increase a person’s risk of sustaining a second ACL injury. However, movement symmetry is not well understood in people who have not had an ACL injury. The goal of this work was to better understand asymmetries in healthy people so that we can better assess those who have suffered an ACL injury. We did this by assessing movement in healthy athletes during single- and double-leg landing activities that have been traditionally used to assess recovery in ACL-R patients. We found that the healthy athletes exhibited significant asymmetries in several metrics during both the single- and double-leg landings. These results indicate that movement symmetry should not be assumed in healthy control subjects. We also similarities and differences in symmetry profiles between single- and double-leg landing activities in a control population. The results of this study will enable researchers to better understand movement deficiencies in ACL-R patients when compared to healthy control subjects as we continue to work to minimize re-injury following return to sport in ACL patients.

No / In this paper we present a constrained inverse kinematics algorithm for real-time motion capture in virtual environments, that has its origins in the simulation of multi-body systems. We apply this algorithm to an articulated human skeletal model using an electromagnetic motion tracking system with a small number of sensors to create avatar postures. The method offers efficient inverse kinematics computation and it is also generalised for the configurations of an articulated skeletal model. We investigate the possibility of capturing fast gestures by analysing the convergence patterns of the algorithm with the motion tracking sampling frequency for a range of actions.

Hand and finger movements are underrepresented in biomechanical studies, primarily due to the challenge of tracking the hands and fingers. Several limitations are associated with marker-based motion capture, including interference with natural movement, and require the tedious, time-consuming application of numerous markers. Advancements in computer vision have led to the development of markerless motion capture systems yet validation of markerless systems for the upper extremities is limited, especially the hand and fingers. The purpose of this study was to develop and assess a markerless motion capture system capable of tracking hand and finger kinematics. A markerless system using four synchronized webcams was developed. Camera pairs were organized in different angles Centre90° (C/90°), Left45°/Right45° (L45°/R45°), and Centre/Left45° (C/L45°). Motion capture was performed with both marker-based and markerless systems. Twenty healthy participants performed five dynamic hand tasks with and without markers. Three-dimensional joint positions were defined using a musculoskeletal model in OpenSim. No significant differences were observed between C/90° and C/L45° markerless camera pairs and the marker-based system. The L45°/R45° camera pair differed significantly from other markerless pairs in several tasks but agreed with the marker-based system for the index finger during flexion. For most of the fingers, no significant differences were found across the different camera pairs. Correlations and error for the concurrent finger flexion task revealed high consistency among all the camera pairs, with R² above 0.90 and RMSD below 10°, the thumb showed greater variability. The R² and RMSD varied depending on the camera comparison and finger for each task. Markerless motion capture for the hands and fingers is possible with little difference to marker-based systems and is dependent on the camera orientation used. / Thesis / Master of Science in Kinesiology