Abstract Background: Osteoarthritis (OA) is a prevalent joint disease causing significant disability, particularly in the knee often treated end-stage with joint replacement surgery. While partial knee arthroplasty (PKA) is noted for quicker recovery and better functionality compared to total knee arthroplasty (TKA), its underutilization highlights a gap in surgical decision-making, driven by a lack of objective data on pre-operative functional differences. Methods: This prospective observational study, conducted from November 2023 to April 2024 at St. Joseph’s Healthcare Hamilton, included 34 end-stage OA patients scheduled for knee arthroplasty. Participants underwent pre-operative functional assessments using markerless motion capture technology to analyze gait and mobility during walking and sit-to-stand tests. Results: The study found no significant differences in basic gait and sit-to-stand metrics between the PKA and TKA groups at a preferred pace. However, at a faster pace, PKA patients demonstrated greater adaptability, showing significant increases in peak stance knee flexion, knee flexion excursions, and stride length, compared to TKA patients whose gait patterns remained consistent across speeds. Conclusion: PKA patients exhibit greater functional adaptability in their pre-operative state, suggesting potential underestimation of their capabilities in current surgical evaluations. Incorporating varied-pace walking tests in pre-operative assessments may provide deeper insights into functional capabilities, influencing more tailored surgical decisions and potentially increasing the application of PKA in suitable candidates. / Thesis / Master of Applied Science (MASc)

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.

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

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.

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.

Motion capture(“MoCap”) has been used for a long time in the movie and videogame industries to animate digital characters. This technology commonly requires a studio and expensive stationary equipment. However, in recent years markerless MoCap has emerged. This is a technology that uses machine learning to estimate and reproduce the movements of humans. This technology can be used with a single video camera thus making it more accessible. This study relates to research on motion capture, machine learning and computer animation. The study examines a selection of markerless MoCap tools available on the market with amateurs and small businesses as target audiences. This to explore to which extent markerless MoCap for amateurs is suitable for use. The research questions asked in this study are: How well do these tools recreate motions from an animation? How are these results affected by aggravating circumstances? How do the results of the tools differ from each other? To explore these questions, a selection of five markerless MoCap services was made. These five services were then tested to study their performances in different aggravating circumstances. An original animation was created and used in these tests. The results from these tests were analyzed using a qualitative visual analysis and a numerical analysis of extreme values. The study found the tools could not accurately reproduce the animation they were given to process. The most prominent problem being that of depth perception, which resulted in the processed animations often deviating in depth. The services also had obvious problems with recreating arms. The study also found that some of the different aggravating circumstances affected the results more than others. The results of this study shows that markerless MoCap for amateurs still has development ahead of it before the technology can be considered an effective tool. / Motion Capture (“MoCap”) har länge använts inom film- och spelindustrin för att animera digitala karaktärer. MoCap i storskalig produktion kräver dock vanligtvis en studio och dyr utrustning. Men på senare år har Markerless MoCap vuxit fram. Det är en teknik som använder sig av maskininlärning för att estimera och avbilda en persons rörelser. Denna kan användas med enbart en videokamera vilket gör tekniken lättillgänglig. Denna studie relaterar till forskning som berör Motion Capture, 3D-datoranimation, AI och maskininlärning. Studien undersöker ett urval av Markerless MoCap-verktyg som finns tillgängliga allmänheten, med amatörer och småföretag som målgrupp. Detta i syfte att undersöka i vilken utsträckning markerless MoCap för amatörer är lämplig för bruk. Problemformuleringen i denna studie är: Hur väl återskapar programmen rörelserna från en animation? Hur påverkas detta resultat av försvårande omständigheter? Hur skiljer sig dessa programs resultat från varandra? För att undersöka dessa frågor gjordes ett urval av fem Markerless MoCap-verktyg. Dessa fem verktyg testades för att studera verktygens prestationer under olika försvårande omständigheter. En egenproducerad animation användes i dessa tester. Resultaten från dessa tester analyserades med en kvalitativ visuell analys och en numerisk analys av extremvärden. Studien fann att verktygen inte med precision kunde återge den animation de fått att avbilda. Tydligast var problemet med djupseendet, vilket resulterade i att de bearbetade animationerna ofta avvek i djupled. Verktygen hade också påtagliga problem med att avbilda armar. Studien fann även att vissa försvårande omständigheter hade större effekt än andra. Den här studiens resultat visar att Markerless MoCap för amatörer fortfarande har utveckling kvar innan tekniken kan betraktas som ett effektivt verktyg.

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Nous nous intéressons à l'acquisition automatique de mouvements 3D de personnes. Cette opération doit être réalisée sans un équipement spécialisé (marqueurs ou habillage spécifique), pour rendre son utilisation générale, sous la contrainte du temps réel. Pour répondre à ces questions, nous sommes amenés à traiter de la reconstruction et l'analyse de la forme 3D. Concernant le problème de reconstruction 3D en temps réel d'entités en mouvement à partir de plusieurs vues, les approches existantes font souvent appel à des calculs complexes incompatibles avec la contrainte du temps réel. Les approches du type SFS offrent un compromis intéressant entre efficacité algorithmique et précision. Ces dernières utilisent les silhouettes issues de chaque caméra pour proposer un volume englobant des objets. Cependant elles nécessitent un environnement particulièrement contraint, dont le placement minutieux des caméras. Les travaux présentés dans ce manuscrit généralisent l'utilisation des approches SFS à des environnements peu contrôlés. L'acquisition du mouvement revient à déterminer les paramètres offrant la meilleure corrélation entre le modèle et la reconstruction 3D. Notre objectif étant le suivi temps réel, nous proposons des méthodes qui offrent la précision requise et le temps réel. Couplé à un suivi temporel par filtre de Kalman, à un recalage d'objets géométriques simples (ellipsoïdes, sphères, etc.), nous proposons un système temps réel, offrant une erreur de l'ordre de 6%.De par sa robustesse, il permet le suivi simultané de plusieurs personnes, même lors de contacts. Les résultats obtenus ouvrent des perspectives à un transfert vers des applications grand public / We aim at automatically capturing 3D motion of persons without markers. To make it flexible, and to consider interactive applications, we address real-time solution, without specialized instrumentation. Real-time body estimation and shape analyze lead to home motion capture application. We begin by addressing the problem of 3D real-time reconstruction of moving objects from multiple views. Existing approaches often involve complex computation methods, making them incompatible with real-time constraints. Shape-From-Silhouette (SFS) approaches provide interesting compromise between algorithm efficiency and accuracy. They estimate 3D objects from their silhouettes in each camera. However they require constrained environments and cameras placement. The works presented in this document generalize the use of SFS approaches to uncontrolled environments. The main methods of marker-less motion capture, are based on parametric modeling of the human body. The acquisition of movement goal is to determine the parameters that provide the best correlation between the model and the 3D reconstruction.The following approaches, more robust, use natural markings of the body extremities: the skin. Coupled with a temporal Kalman filter, a registration of simple geometric objects, or an ellipsoids' decomposition, we have proposed two real-time approaches, providing a mean error of 6%. Thanks to the approach robustness, it allows the simultaneous monitoring of several people even in contacts. The results obtained open up prospects for a transfer to home applications

Although upper limb motor impairments are common, the primary tools for assessing and tracking these impairments in a clinical setting are subjective, qualitative rating scales that lack resolution and repeatability. Markerless motion capture technology has the potential to greatly improve clinical assessment by providing quick, low-cost, and accurate tools to objectively quantify motor deficits. Here we lay some of the groundwork necessary to enable markerless motion capture systems to be used in clinical settings. First, we adapted five motor tests common in clinical assessments so they can be administered via markerless motion capture. We implemented these modified tests using a particular motion capture sensor (Leap MotionTM Controller, hereafter referred to as the Leap Motion sensor) and administered the tests to 100 healthy subjects to evaluate the feasibility of administrating these tests via markerless motion capture. Second, to determine the ability of the Leap Motion sensor to accurately measure tremor, we characterized the frequency response of the Leap Motion sensor. During the administration of the five modified motor tests on 100 healthy subjects, the subjects had little trouble interfacing with the Leap Motion sensor and graphical user interface, performing the tasks with ease. The Leap Motion sensor maintained an average sampling rate above 106 Hz across all subjects during each of the five tests. The rate of adverse events caused by the Leap Motion sensor (mainly jumps in time or space) was generally below 1%. In characterizing the frequency response of the Leap Motion sensor, we found its bandwidth to vary between 1.7 and 5.5 Hz for actual tremor amplitudes above 1.5 mm, with larger bandwidth for larger amplitudes. To improve the accuracy of tremor measurements, we provide the magnitude ratios that can be used to estimate the actual amplitude of the oscillations from the measurements by the Leap Motion sensor. These results suggest that markerless motion capture systems are on the verge of becoming suitable for routine clinical use, but more work is necessary to further improve the motor tests before they can be administered via markerless motion capture with sufficient robustness for clinical settings.