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The study of human motion from a medical standpoint has traditionally involved the use of marker-based motion tracking systems, as well as other sensory devices. This equipment is often expensive, has low-portability, and might even influence tracking results by distracting or otherwise inhibiting a subject's normal motion performance. In comparison, non-marker-based tracking methods are less costly, easier to move and set up, and requires no markers or other devices. However, previous work in silhouette-based human motion analysis is typically focused on the classification of activities or the identification of subjects, neither of which are much use to medical professionals. We propose a merging of these two research fields. By applying silhouette-based motion tracking to the problem of motion performance analysis, we have developed a new method which can reliably and accurately model human motions and detect abnormalities. Our approach, which is based on Hidden Markovian Models with continuous observation probabilities, creates standardized models to represent common human motions. These models are then used as a basis for further analysis. We have extensively tested the proposed method with a custom designed database that takes into account speed related and subject related variations of motion performance. / Graduate

The Sit-to-Stand (STS) transition is a voluntary daily activity that consists of rising from a sitting position to a standing position, an activity that is typically performed by a person several times a day. To undertake the activity successfully requires the coordination of the body limbs in order to transfer the body weight between the sitting and standing positions, maintaining the balance, in order to avoid a fall. A biomechanical analysis of the STS transition provides useful information about the motor ability and control strategy of a person and as such, it is commonly employed to assess functional performance, and as an indicator of lower limb strength in the elderly and in people with disabling diseases. The aim of the work described in this thesis was to investigate and analyse the STS transition in two groups of healthy subjects, a cohort (n=10) of younger adult participants (age range 28±2 years) and a cohort (n=10) of older adult participants (age range 56±8 years), in order to identify the differences in the performances within and between the two groups when the STS transition was undertaken at different speeds. The two groups of participants performed STS transition trials at three, different, self-selected speeds (normal, slow and fast) during which data was recorded from a caption systems, consisting of a set of six infrared-cameras and two force plates. The in-vivo data obtained was applied to a link segment biomechanical model enabling the kinematic contribution of the major body segments to the STS activity to be determined for each participant. A principal component analysis (PCA) was undertaken to identify any aggregate and segmental differences in the STS transition performance between speeds. In addition, a kinetic analysis was performed to determine the torque and power contributions of the lower limb joints during the STS transition. The results from the analysis showed that younger and older participants performed the STS transition with a similar pattern, but they used different strategies to ascend according to the speed at which the activity was being performed. The younger participants used the same strategy at slow speed than the older participants used at slow and normal speeds. Likewise, the younger participants used the same strategy at normal and fast speeds as the older participants used at fast speed. From the segmental analysis it was found that the upper-body and pelvis segments presented the larger variability than the other segments. From the joint analysis, the knee and hip joints were identified as the joints that provide the greatest contribution to the STS transition as they generated most of the power and torque required for the activity. The results obtained and the methodology developed could help clinicians with the diagnosis, planning and selection of treatment for patients with a lack of mobility. This type of analysis may also find application in fields such as robotics, ergonomics and sports training.

This thesis introduces locomotion synthesis methods for humanoid characters. Motion synthesis is an under-constrained problem that requires additional constraints beyond user inputs. Two main approaches to introducing additional constraints are physics-based and data-driven. Despite significant progress in the past 20 years, major difficulties still exist for both approaches. In general, building animation systems that are flexible to user requirements while keeping the synthesized motions plausible remain a challenging task. The methods introduced in this thesis, presented in two-parts, aim to allow animation systems to be more flexible to user demands without radically violating constraints that are important for maintaining plausibility. In the first part of the thesis, we address an important subproblem in physics-based animation --- controller synthesis for humanoid characters. We describe a method for optimizing the parameters of a physics-based controller for full-body, 3D walking. The objective function includes terms for power minimization, angular momentum minimization, and minimal head motion, among others. Together these terms produce a number of important features of natural walking, including active toe-off, near-passive knee swing, and leg extension during swing. We then extend the algorithm to optimize for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. Controller optimization entails optimizing the expected value of the objective function, which is computed by Monte Carlo methods. We demonstrate examples with a variety of sources of uncertainty and task constraints. The second part of this thesis deals with the data-driven approach and the problem of motion modeling. Defining suitable models for human motion data is non-trivial. Simple linear models are not expressive enough, while more complex models require setting many parameters and are difficult to learn with limited data. Using Bayesian methods, we demonstrate how the Gaussian process prior can be used to derive a kernelized version of multilinear models. The result is a locomotion model that takes advantage of training data addressed by multiple indices to improve generalization to unseen motions.

The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots. We introduce an approach for estimating the walking direction of people from images, based on learning the correct classification of a still image by using SVMs. We find that the performance of the system can be improved by classifying each image of a walking sequence and combining the outputs of the classifier. Experiments were performed to evaluate our system and estimate the trade-off between number of images in walking sequences and performance.

This thesis treats the subject of applying human motion models to create estimators for the input signals of human operators controlling a telerobotic system.In telerobotic systems, the control signal input by the operator is often treated as a known quantity. However, there are instances where this is not the case. For example, a well-studied problem is teleoperation under time delay, where the robot at the remote site does not have access to current operator input due to time delays in the communication channel. Another is where the hardware sensors in the input device have low accuracy. Both these cases are studied in this thesis. A solution to these types of problems is to apply an estimator to the input signal. There exist several models that describe human hand motion, and these can be used to create a model-based estimator. In the present work, we propose the use of the minimum jerk (MJ) model. This choice of model is based mainly on the simplicity of the MJ model, which can be described as a fifth degree polynomial in the cartesian space of the position of the subject's hand. Estimators incorporating the MJ model are implemented and inserted into control systems for a teleoperatedrobot arm. We perform experiments where we show that these estimators can be used for predictors increasing task performance in the presence of time delays. We also show how similar estimators can be used to implement direct position control using a handheld device equipped only with accelerometers. / QC 20100810

A testing framework was developed to address system spatial and temporal performance characteristics in a two-dimensional (2D) human motion analysis system using commercially available digital video capture. The first testing protocol involved developing a method to evaluate system spatial performance characteristics with respect to accuracy, precision, and resolution. A physical model comprising a calibration frame was constructed with phantom postures selected to represent joint angles and off-plane movement typical of the activities of interest. This provided reference angles to which angles measured from digitally captured images were compared using the Bland and Altman method. Validation experiments confirmed that the principal sources of error were due to off-plane motion and pixel resolution in the video capture and analysis systems. In these analyses, it was verified that simulated experimental conditions could be corrected using the direct linear transform (DLT); however, the removal of parallax still resulted in 2 degrees of error in measured joint angles. The main source of error was resolution of the data acquisition system verified through Monte Carlo simulations. The second testing protocol involved developing a simple method to determine the temporal accuracy of motion analysis systems incorporating digital video cameras and a pendulum. A planar column pendulum with a natural frequency of 0.872 Hz was used to analyse five systems incorporating commercially available cameras and a single codec. The frame rate for each camera was measured to be within 3% of the US National Television Systems Committee (NTSC) broadcasting digital video standard of 29.97 fps.; however some cameras produced a frame duplication artefact. Least squares curve-fitting using a sinusoidal function revealed RMS differences between 3-5% for angular position and 5-15% for angular speed compared to the captured motion data. It was shown that some digital-video cameras and computer playback software contain data compression technology that may produce substantial temporal frame inaccuracies in recovered video sequences and that temporal accuracy should be evaluated in digital-based human motion analysis systems prior to their use in experimentation. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2009-08-14 10:54:58.685

Chopin’s Etudes are difficult musical compositions for advanced piano students. Helmut Brauss, a professional pianist and educator, has created a number of videos to teach students motion patterns that will help them perfect the Etudes. The subtleties of motion shown in the videos are not apparently obvious to students, and in our research, we have developed four markerless based approaches to visually augment the videos: Predictive Optical Flow, Historical Optical Flow, Predictive Hand Tracking and Historical Hand Tracking. A survey of students learning the Etudes was conducted, and it was determined that the participants found the Historical techniques to be the most useful. No difference could be found between the usefulness of the Optical Flow and Hand Tracking augmentations. / Graduate

This thesis explores the use of physics based human motion models in the context of video-based human motion estimation and scene analysis. Two abstract models of human locomotion are described and used as the basis for video-based estimation. These models demonstrate the power of physics based models to provide meaningful cues for estimation without the use of motion capture data. However promising, the abstract nature of these models limit the range of motion they can faithfully capture. A more detailed model of human motion and ground interaction is also described. This model is used to estimate the ground surface which a subject interacts with, the forces driving the motion and, finally, to smooth corrupted motions from existing trackers in a physically realistic fashion. This thesis suggests that one of the key difficulties in using physical models is the discontinuous nature of contact and collisions. Two different approaches to handling ground contacts are demonstrated,one using explicit detection and collision resolution and the other using a continuous approximation. This difficulty also distinguishes the models used here from others used in other areas which often sidestep the issue of collisions.