It is my thesis that a cost-effective motion capture system for wireless 3D controllers can be developed through the use of low-cost inertial measurement devices and camera systems. Current optical motion capture systems require a number of expensive high-speed cameras. The use of such systems is impractical for many applications due to its high cost. This is particularly true for consumer-level wireless 3D controllers. More importantly, optical systems are capable of directly tracking an object with only three degrees of freedom. The proposed system attempts to solve these issues by combining a low-cost camera system with low-cost micro-machined inertial measurement devices such as accelerometers and gyro sensors to provide accurate motion tracking with a full six degrees of freedom. The proposed system combines the data collected from the various sensors in the system to obtain position information about the wireless 3D controller with 6 degrees of freedom. The system utilizes a number of calibration, error correction, and sensor fusion techniques to accomplish this task. The key advantage of the proposed system is that it combines the high long-term accuracy and low frequency nature of the camera system and complements it with the low long-term accuracy and high frequency nature of the inertial measurement devices to produce a system with a high level of long-term accuracy with detailed high frequency information about the motion of the wireless 3D controller.

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

The Niagara Foot (NF) is a relatively new prosthetic design, primarily intended for use in developing countries. It combines low cost and durability with high performance energy return features. The design has been successfully tested mechanically and in field trials, but to date there has been little quantitative gait data describing the performance of the foot. Biomechanical gait analysis techniques will be used to extract quantitative gait measures. The current study is designed to characterize the effect of heel section stiffness parameter differences between a NF normal heel and a NF with a reduced material heel section., on gait characteristics in persons with unilateral trans-tibial amputations (TTA). Standardized biomechanical gait analysis techniques, adapted for this population, were used to extract quantitative gait measures. Five persons with TTA performed walking tasks while 3D ground reaction forces were recorded via an embedded force platform. A motion capture system also recorded the 3D segmental motion of the lower limbs and torso of each subject. These were combined to calculate net joint moments and mechanical power at the hip and knee of both limbs. These data were compared between a normal NF and a NF with a modified heel. Each participant had a period of two-week adaptation prior to any testing. An EMG system and a prosthesis evaluation questionnaire were used to help analyze the condition. The overall hypothesis of this study was that modification of the heel section stiffness would change several aspects of gait. Although the gait pattern differences between participants and the low participant number produced no significant differences between the conditions for all variables, trends were observed in multiple outcomes. These results report preliminary evidence that for some participants the heel material reduction does impact their gait by showing a different loading phase during the transition between the heel strike and the full contact with the ground. The NF2 may move the gait toward a more flexed knee position. Furthermore, despite a reduction in the material of the heel section results showed that the overall foot stiffness increased. This may be the result of the one-piece design and mechanics of the NF. Further investigations with a bigger cohort of people with TTA are required to look at the importance of the impact of the prosthetic foot heel stiffness.

In this study, we developed an animation tool to animate 3D virtual characters. The tool offers facilities to integrate motion capture data with a 3D character mesh and animate the mesh by using Skeleton Subsurface Deformation and Dual Quaternion Skinning Methods. It is a compact tool, so it is possible to distribute, install and use the tool with ease. This tool can be used to illustrate medical kinematic gait data for educational purposes. For validation, we obtained medical motion capture data from two separate sources and animated a 3D mesh model by using this data. The animations are presented to physicians for evaluation. The results show that the tool is sufficient in displaying obvious gait patterns of the patients. The tool provides interactivity for inspecting the movements of patient from different angles and distances. We animate anonymous virtual characters which provide anonymity of the patient.

<p>Motion capture is the process of capturing movements from real life into a computer. Existing motion capture systems are often very expensive and require advanced hardware that makes the process complex. This thesis will answer the following question: is it possible to create an optical motion capture system using only a single low cost Dvcamera (Digital Video Camera), that still will produce accurate motion capture data? To answer this question and construct our motion capture system we need to complete these following steps:</p><p>• Create a usable film sequence.</p><p>• Analyze the sequence.</p><p>• Create motion capture data.</p><p>• Apply the motion capture data for 3D character and analyze the outcome.</p><p>The method chosen for this thesis is constructive research. In short terms it is the study of whether we can or cannot build a new artifact. The following theoretic tools were used in the process of creating a motion capture system: Color theory, RGB, Connected component labeling, Skeletons in 3D animation, Calculating angels using trigonometry, .x files and Quaternions. We have found that an optical motion capture system is very complex and it is hard to produce as a low budget system. Our attempt did not live up to our expectations. The idea with using only one DV camera was to simplify the system since it would require no calibration or syncronisation. It would also make the system cost efficient and more available to the general public. The single camera solution unfortunatly created a number of problems in our system. Our system does however work with less complex movements. It can produce motion capture data that is accurate enough to be used in low budget games. It is also cost effective compared to other systems on the market. The system has a very easy setup and does not need any calibration in addition to the init position.</p>

This thesis presents a new human-like upper limb and hand motion generating method. The work is based on Functional Principal Component Analysis and Quadratic Programming. The human-like motion generating problem is formulated in a framework of minimizing the difference of the dynamic profile of the optimal trajectory and the known types of trajectory. Statistical analysis is applied to the pre-captured human motion records to work in a low dimensional space. A novel PCA FPCA hybrid motion recognition method is proposed. This method is implemented on human grasping data to demonstrate its advantage in human motion recognition. One human grasping hierarchy is also proposed during the study. The proposed method of generating human-like upper limb and hand motion explores the ability to learn the motion kernels from human demonstration. Issues in acquiring motion kernels are also discussed. The trajectory planning method applies different weight on the extracted motion kernels to approximate the kinematic constraints of the task. Multiple means of evaluation are implemented to illustrate the quality of the generated optimal human-like trajectory compared to the real human motion records.

Technologies in digital cinema are quickly changing the way contemporary filmmakers create films and how audiences currently perceive them. As we move onward into the digital turn, it becomes ever more apparent that the medium of film has been emancipated from its dependence on the photograph. Directors are no longer required to capture the objectively real as it sits before the photographic lens, but can essentially construct it via groundbreaking advancements in computer-generated imagery, motion capture technology, and digital 3D camera systems and display technologies. Since the origins of film, spectators and filmmakers have assumed an existing relationship between reality and the photographic image. Yet digital film technologies now provide us with hyper-facsimiles of reality that are perceived as photographic, but are often created by way of computer processes. Digital cinema currently allows the viewer to inhabit and interact with cinematic realities in unprecedented ways, and it is this contemporary paradigmatic shift from the analog to the digital that has catalyzed fundamentally new ways of looking at the filmic image. In this paper, I will examine the perceptual complexities of contemporary digital film through the lens of these cinematic technologies by examining their impact on the viewer’s experience.

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

3D Motion capture is a medium that plots motion, typically human motion, converting it into a form that can be represented digitally. It is a fast evolving field and recent inertial technology may provide new artistic possibilities for its use in live performance. Although not often used in this context, motion capture has a combination of attributes that can provide unique forms of collaboration with performance arts. The inertial motion capture suit used for this study has orientation sensors placed at strategic points on the body to map body motion. Its portability, real-time performance, ease of use, and its immunity from line-of-sight problems inherent in optical systems suggest it would work well as a live performance technology. Many animation techniques can be used in real-time. This research examines a broad cross-section of these techniques using four practice-led cases to assess the suitability of inertial motion capture to live performance. Although each case explores different visual possibilities, all make use of the performativity of the medium, using either an improvisational format or interactivity among stage, audience and screen that would be difficult to emulate any other way. A real-time environment is not capable of reproducing the depth and sophistication of animation people have come to expect through media. These environments take many hours to render. In time the combination of what can be produced in real-time and the tools available in a 3D environment will no doubt create their own tree of aesthetic directions in live performance. The case study looks at the potential of interactivity that this technology offers.

Biometrics is a unique physical or behavioral characteristic of a person. This unique attribute, such as fingerprints or gait, can be used for identification or verification purposes. Gait is an emerging biometrics with great potential. Gait recognition is based on recognizing a person by the manner in which they walk. Its potential lays in that it can be captured at a distance and does not require the cooperation of the subject. This advantage makes it a very attractive tool for forensic cases and applications, where it can assist in identifying a suspect when other evidence such as DNA, fingerprints, or a face were not attainable. Gait can be used for recognition in a direct manner when the two samples are shot from similar camera resolution, position, and conditions. Yet in some cases, the only sample available is of an incomplete gait cycle, low resolution, low frame rate, a partially visible subject, or a single static image. Most of these conditions have one thing in common: static measurements. A gait signature is usually formed from a number of dynamic and static features. Static features are physical measurements of height, length, or build; while dynamic features are representations of joint rotations or trajectories. The aim of this thesis is to study the potential of predicting dynamic features from static features. In this thesis, we have created a database that utilizes a 3D laser scanner for capturing accurate shape and volumes of a person, and a motion capture system to accurately record motion data. The first analysis focused on analyzing the correlation between twenty-one 2D static features and eight dynamic features. Eleven pairs of features were regarded as significant with the criterion of a P-value less than 0.05. Other features also showed a strong correlation that indicated the potential of their predictive power. The second analysis focused on 3D static and dynamic features. Through the correlation analysis, 1196 pairs of features were found to be significantly correlated. Based on these results, a linear regression analysis was used to predict a dynamic gait signature. The predictors chosen were based on two adaptive methods that were developed in this thesis: "the top-x" method and the "mixed method". The predictions were assessed for both for their accuracy and their classification potential that would be used for gait recognition. The top results produced a 59.21% mean matching percentile. This result will act as baseline for future research in predicting a dynamic gait signature from static features. The results of this thesis bare potential for applications in biomechanics, biometrics, forensics, and 3D animation.