Published Article / The objective of this study was to examine the relationship between external markers typically used in external motion capturing devices and the true vertebral kinematics in the cervical spine. Twenty one healthy subjects were subjected to low dosage X-rays in five different positions, while radio opaque markers were attached to the skin at each vertebral level. Distance and angle parameters were constructed for vertebral prediction from skin surface markers. The causes of variation in these parameters were identified by investigating the correlations of these parameters with anthropometrical variables. Strong correlations of the parameters were observed in flexion, but in extension, especially full extension, the correlations were poor to insignificant. In neutral, half flexion, and full flexion it is possible to predict the vertebral position from surface markers by using the parameters and anthropometrical variables. In half extension this prediction is less accurate and in full extension alternative methods should be investigated for external motion capturing.

Zugl.: Kassel, Univ., Diss., 2008

We consider the computation of optimal alignments between sub-sequences of given motion clips based on dynamic programming. To avoid redundant computations in multiple alignment queries, we introduced the concept of a guideboard matrix. Using this data structure, the existence inquiry for an alignment is answered in O(1) time and the extraction of the alignment is done in O(n+m) time where n and m denote the numbers of frames in the considered motions.

Der Einsatz digitaler Menschmodelle in der Produkt- und Prozessergonomie hat in den letzten Jahren beständig zugenommen. Vor allem Anforderungen aus dem industriellen Umfeld setzten hohe Maßstäbe an Schnelligkeit, Genauigkeit und Verlässlichkeit der verwendeten Systeme, Methoden und Verfahren. Das biomechanische Menschmodell Dynamicus ist eine am Institut für Mechatronik e.V., Chemnitz entwickelte Software, die sich auf dieses Gebiet der Simulation spezialisiert hat. Die Grundlage von Dynamicus-Simulationen sind reale menschliche Bewegungen, die mit Hilfe eines Motion-Capture-Systems aufgezeichnet werden. Die Analyse der digital vorliegenden Bewegungen erfolgt in den Wissenschaftsgebieten der Ergonomie, des Sports und der Rehabilitation.

We consider the computation of optimal alignments between sub-sequences of given motion clips based on dynamic programming. To avoid redundant computations in multiple alignment queries, we introduced the concept of a guideboard matrix. Using this data structure, the existence inquiry for an alignment is answered in O(1) time and the extraction of the alignment is done in O(n+m) time where n and m denote the numbers of frames in the considered motions.

Der Einsatz digitaler Menschmodelle in der Produkt- und Prozessergonomie hat in den letzten Jahren beständig zugenommen. Vor allem Anforderungen aus dem industriellen Umfeld setzten hohe Maßstäbe an Schnelligkeit, Genauigkeit und Verlässlichkeit der verwendeten Systeme, Methoden und Verfahren. Das biomechanische Menschmodell Dynamicus ist eine am Institut für Mechatronik e.V., Chemnitz entwickelte Software, die sich auf dieses Gebiet der Simulation spezialisiert hat. Die Grundlage von Dynamicus-Simulationen sind reale menschliche Bewegungen, die mit Hilfe eines Motion-Capture-Systems aufgezeichnet werden. Die Analyse der digital vorliegenden Bewegungen erfolgt in den Wissenschaftsgebieten der Ergonomie, des Sports und der Rehabilitation.

Modern motion capturing technologies are capable of collecting quantitative, biomechanical data on golf swings that can help to improve our understanding of golf theory and facilitate the establishing of new, optimized swing paradigms.This study explored the possibility of utilizing Microsoft’s Kinect sensor to analyse the biomechanics of golf swings. Following design-science research principles, it presents a software prototype capable of capturing, recording, analysing and comparing movement patterns using three-dimensional vector angles. The tracking accuracy and data validity of the software were then evaluated in a set of experiments in optimal and real-world conditions using actual golf swing recordings.The results indicate that the software is providing accurate data on joint vector angles with a clear profile view, while visually occluded and frontal angles are more difficult to determine precisely. The employed position detection algorithm demonstrated good results in both optimal and real-world environments. Overall, the presented software and its approach to position analysis and detection show great potential for use in further research efforts. / Program: Magisterutbildning i informatik

Thesis (MScEng (Industrial Engineering))--Stellenbosch University, 2008. / In the context of intervertebral disc replacement and customized implants, human simulation studies are of great importance. Simulation models need input data. This study investigated different in vivo motion capturing methods to capture spinal kinematics that will serve as input for simulation models. Available scanning and motion capturing techniques for capturing cervical kinematics range from simple clinical methods, to expensive specialized equipment and software. With a variety of technologies comes a variety of applications. In this study the focus is on capturing the kinematics of the cervical spine. An important distinction was made between two types of motion capturing technologies: external motion capturing and internal imaging technologies. The available external motion capturing technologies pose many advantages in terms of cost, safety, simplicity, portability and producing accurate three dimensional position and orientation. However, the ability for external motion capturing technologies to give accurate information on the movements at each vertebral level is doubted by critics reasoning that the true vertebral motion is concealed by the skin and soft tissue. Although it would be ideal to use external motion capturing systems, one needs to be confident that these surface markers or sensors truly reflect the vertebral motion at each vertebral level. An empirical study was conducted to evaluate the relationship between motion captured on the skin surface and motion of the vertebrae. Twenty-one subjects received low dosage X-rays, while radio opaque markers were attached to the skin at each respective vertebral level. The motion of external markers and that of the vertebrae could be seen simultaneously on one medium. In the empirical study, two outputs were achieved. Firstly, intervertebral kinematic data, for use in further simulation studies was obtained. Secondly, the relationship between surface markers and vertebrae in different motion instances was investigated. Distance and angle parameters were constructed for vertebral prediction from skin surface markers. The causes of variation in these parameters were identified by investigating the correlations of these parameters with anthropometrical variables. Strong correlations of the parameters were observed in flexion, but in extension, especially full extension, the correlations were poor to insignificant. It was concluded that in neutral, half flexion and full flexion it is possible to predict the vertebral position from surface markers by using the parameters and anthropometrical variables. In half extension this prediction would be less accurate and in full extension alternative methods should be investigated for external motion capturing.

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution.

This thesis outlines the dynamic system analysis of a 3D Ultrasonic neuro- navigation system for use in motion capture studies. The work entails the development and implementation of methods for achieving the same. The objective of the project is to come up with an accurate dynamic 3D ultrasonic neuro-navigation system which can deliver up to sub mm accuracy within the operating workspace for use in image guided neuro surgery. The major focus of the work is to come up with a second order Kalman filter which can take out the outliers occurring in a static system in real time, thereby making the system more robust and accurate. Once the filter achieves the requisites, it can be integrated into the current motion tracking software which allows for the real time tracking of transmitters, hence the points of interest.