Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data

Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers, k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures

An Efficient Fusion Scheme for Human Hand Trajectory Reconstruction Using Inertial Measurement Unit and Kinect Camera

The turn of 21st century has witnessed an evolving trend in wearable devices research and improvements in human-computer interfaces. In such systems, position information of human hands in 3-D space has become extremely important as various applications require knowledge of user’s hand position. A promising example of which is a wearable ring that can naturally and ubiquitously reconstruct handwriting based on motion of human hand in an indoor environment. A common approach is to exploit the portability and affordability of commercially available inertial measurement units (IMU). However, these IMUs suffer from drift errors accumulated by double integration of acceleration readings. This process accrues intrinsic errors coming from sensor’s sensitivity, factory bias, thermal noise, etc., which result in large deviation from position’s ground truth over time. Other approaches utilize optical sensors for better position estimation, but these sensors suffer from occlusion and environment lighting conditions. In this thesis, we first present techniques to calibrate IMU, minimizing undesired effects of intrinsic imperfection resided within cheap MEMS sensors. We then introduce a Kalman filter-based fusion scheme incorporating data collected from IMU and Kinect camera, which is shown to overcome each sensor’s disadvantages and improve the overall quality of reconstructed trajectory of human hands

A calibration method for MEMS inertial sensors based on optical techniques.

Dong, Zhuxin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-80).Abstracts in English and Chinese.Abstract --- p.ii摘要 --- p.iiiAcknowledgements --- p.ivTable of Contents --- p.vList of Figures --- p.viiList of Tables --- p.ixChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Architecture of UDWI --- p.3Chapter 1.2 --- Background of IMU Sensor Calibration --- p.5Chapter 1.3 --- Organization --- p.7Chapter Chapter 2 --- 2D Motion Calibration --- p.10Chapter 2.1 --- Experimental Platform --- p.10Chapter 2.1.1 --- Transparent Table --- p.10Chapter 2.2 --- Matching Algorithm --- p.13Chapter 2.2.1 --- Motion Analysis --- p.13Chapter 2.2.2 --- Core Algorithm and Matching Criterion --- p.14Chapter 2.3 --- Usage of High Speed Camera --- p.17Chapter 2.4 --- Functions Realized --- p.17Chapter Chapter 3 --- Usage of Camera Calibration --- p.21Chapter 3.1 --- Introduction to Camera Calibration --- p.21Chapter 3.1.1 --- Related Coordinate Frames --- p.21Chapter 3.1.2 --- Pin-Hole Model --- p.24Chapter 3.2 --- Calibration for Nonlinear Model --- p.27Chapter 3.3 --- Implementation of Process to Calibrate Camera --- p.28Chapter 3.3.1 --- Image Capture --- p.28Chapter 3.3.2 --- Define World Frame and Extract Corners --- p.28Chapter 3.3.3 --- Main Calibration --- p.30Chapter 3.4 --- Calibration Results of High Speed Camera --- p.33Chapter 3.4.1 --- Lens Selection --- p.33Chapter 3.4.2 --- Property of High Speed Camera --- p.34Chapter Chapter 4 --- 3D Attitude Calibration --- p.36Chapter 4.1 --- The Necessity of Attitude Calibration --- p.36Chapter 4.2 --- Stereo Vision and 3D Reconstruction --- p.37Chapter 4.2.1 --- Physical Meaning and Mathematical Model Proof --- p.37Chapter 4.2.2 --- 3D Point Reconstruction --- p.38Chapter 4.3 --- Example of 3D Point Reconstruction --- p.40Chapter 4.4 --- Idea of Attitude Calibration --- p.42Chapter Chapter 5 --- Experimental Results --- p.45Chapter 5.1 --- Calculation of Proportional Parameter --- p.45Chapter 5.2 --- Accuracy Test of Stroke Reconstruction --- p.46Chapter 5.3 --- Writing Experiments of 26 Letters --- p.47Chapter 5.3.1 --- Experimental Results of Letter b --- p.48Chapter 5.3.2 --- Experimental Results of Letter n with ZVC --- p.51Chapter 5.3.3 --- Experimental Results of Letter u --- p.54Chapter 5.4 --- Writing of Single Letter s - Multiple Tests --- p.56Chapter 5.5 --- Analysis on Resolution Property of Current Vision Algorithm --- p.58Chapter 5.5.1 --- Resolution of Current Algorithm --- p.58Chapter 5.5.2 --- Tests with Various Filters --- p.59Chapter 5.6 --- Calculation of Static Attitude --- p.61Chapter Chapter 6 --- Future Work --- p.64Chapter 6.1 --- Another Multiple Tests of Letter k --- p.64Chapter 6.2 --- Letter Recognition Based on Neural Networks Classification --- p.66Chapter Chapter 7 --- Conclusion --- p.69Chapter 7.1 --- Calibration ofMAG-μlMU Sensors --- p.69Chapter 7.2 --- Calibration of Accelerometers --- p.70Chapter 7.3 --- Calibration of Attitude --- p.70Chapter 7.4 --- Future Work --- p.71Appendix A The Experimental Results of Writing English Letters --- p.7

An attitude compensation technique for a MEMS motion sensor based digital writing instrument.

Luo Yilun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 87-91).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1. --- Organization --- p.3Chapter 2. --- Architecture of MAG-μIMU --- p.5Chapter 2.1. --- Hardware for Attitude Filter --- p.5Chapter 2.2. --- Handwriting Recording for a Digital Writing Instrument --- p.7Chapter 3. --- Inertial Tracking for Handwriting --- p.9Chapter 3.1. --- Spatial Descriptions and Transformations --- p.9Chapter 3.1.1. --- Vector Description and Position of a Frame --- p.9Chapter 3.1.2. --- Coordinate Transformation and Orientation of a Frame --- p.10Chapter 3.1.3. --- Kinematics for Digital Writing Instruments --- p.12Chapter 3.1.4. --- Vector Rotation --- p.16Chapter 3.2. --- Euler Angles for Rotation in Space --- p.17Chapter 3.3. --- Euler Angles Attitude Kinematics --- p.19Chapter 3.4. --- Singular Problem --- p.19Chapter 4. --- Attitude in Quaternion --- p.22Chapter 4.1. --- Quaternion Operations --- p.22Chapter 4.1.1. --- Quaternion Conjugate --- p.23Chapter 4.1.2. --- Quaternion Norm --- p.24Chapter 4.1.3. --- Quaternion Inverse --- p.24Chapter 4.2. --- Orientation Description in Quaternion --- p.24Chapter 4.3. --- Attitude Kinematics in Quaternion --- p.25Chapter 5. --- Kalman Filter --- p.27Chapter 5.1. --- Time Update --- p.28Chapter 5.2. --- Measurement Update --- p.29Chapter 5.2.1. --- Maximum a Posterior Probability --- p.29Chapter 5.2.2. --- Batch Least-Square Estimation --- p.31Chapter 5.2.3. --- Measurement Update in Kalman Filter --- p.34Chapter 5.3. --- Kalman Filter Summary --- p.36Chapter 6. --- Extended Kalman Filter --- p.38Chapter 7. --- Attitude Extended Kalman Filter --- p.41Chapter 7.1. --- Time Update Model --- p.41Chapter 7.1.1. --- Attitude Strapdown Theory for a Quaternion --- p.41Chapter 7.1.2. --- Error Model for Time Update --- p.42Chapter 7.2. --- Measurement Update Model --- p.43Chapter 7.2.1. --- Error Model for the Measurement Update --- p.45Chapter 7.3. --- Summary --- p.46Chapter 8. --- Experiment Results --- p.47Chapter 8.1. --- Experiment for Attitude EKF based on MAG-μIMU --- p.47Chapter 8.1.1. --- Simulation Test --- p.48Chapter 8.1.2. --- Experiment Test --- p.49Chapter 8.2. --- Writing Application based on Attitude EKF Compensation --- p.52Chapter 8.2.1. --- Stroke Segment Kalman Filter --- p.54Chapter 8.2.2. --- Zero Velocity Compensation --- p.58Chapter 8.2.3. --- Complementary Attitude EKF for Writing Experiment --- p.60Chapter 9. --- Future Work --- p.73Chapter 9.1. --- Unscented Kalman Filter --- p.73Chapter 9.1.1. --- Least-square Estimator Structure --- p.73Chapter 9.1.2. --- Unscented Transform --- p.74Chapter 9.1.3. --- Unscented Kalman Filter --- p.76Chapter 9.2. --- Experiment Result --- p.81Chapter 10. --- Conclusion --- p.85Chapter 10.1. --- Attitude Extended Kalman Filter --- p.85Chapter 10.2. --- Complementary Attitude EKF --- p.85Chapter 10.3. --- Unscented Kalman Filter --- p.86Chapter 10.4. --- Future Work --- p.86Bibliography --- p.87Appendix A --- p.9

Doctor of Philosophy

dissertationThe need for position and orientation information in a wide variety of applications has led to the development of equally varied methods for providing it. Amongst the alternatives, inertial navigation is a solution that o ffers self-contained operation and provides angular rate, orientation, acceleration, velocity, and position information. Until recently, the size, cost, and weight of inertial sensors has limited their use to vehicles with relatively large payload capacities and instrumentation budgets. However, the development of microelectromechanical system (MEMS) inertial sensors now o ers the possibility of using inertial measurement in smaller, even human-scale, applications. Though much progress has been made toward this goal, there are still many obstacles. While operating independently from any outside reference, inertial measurement su ers from unbounded errors that grow at rates up to cubic in time. Since the reduced size and cost of these new miniaturized sensors comes at the expense of accuracy and stability, the problem of error accumulation becomes more acute. Nevertheless, researchers have demonstrated that useful results can be obtained in real-world applications. The research presented herein provides several contributions to the development of human-scale inertial navigation. A calibration technique allowing complex sensor models to be identified using inexpensive hardware and linear solution techniques has been developed. This is shown to provide significant improvements in the accuracy of the calibrated outputs from MEMS inertial sensors. Error correction algorithms based on easily identifiable characteristics of the sensor outputs have also been developed. These are demonstrated in both one- and three-dimensional navigation. The results show significant improvements in the levels of accuracy that can be obtained using these inexpensive sensors. The algorithms also eliminate empirical, application-specific simplifications and heuristics, upon which many existing techniques have depended, and make inertial navigation a more viable solution for tracking the motion around us

Construction kit for computationally enabled textiles

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 87-89).As technology moves forward, electronics have enmeshed with every aspect of daily life. Some pioneers have also embraced electronics as a means of expression and exploration, creating the fields of wearable computing and electronic textiles. While wearable computing and electronic textiles seem superficially connected as fields of investigation, in fact they are currently widely separated. However, as the field of electronic textiles grows and matures, it has become apparent that better tools and techniques are necessary in order for artists and designers interested in using electronic textiles as a means of expression and function to be able to use the full capabilities of the available technology. It remains generally outside the reach of the average designer or artist to create e-textile experiences, thus preventing them from appropriating the technology, and in turn allowing the general public to accept and exploit the technology. There is clearly a need to facilitate this cross-pollination between the technical and design domains both in order to foster greater creativity and depth in the field of electronic textiles, and in order to bring greater social acceptability to wearable computing.(cont.) This thesis introduces behavioral textiles, the intersection of wearable computing and electronic textiles that brings the interactive capability of wearable electronics to electronic textiles. As a means of harnessing this capability, the thesis also presents subTextile, a powerful and novel visual programming language and development. Design guidelines for hardware that can be used with the development environment to create complete behavioral textile systems are also presented. Using a rich, goal-oriented interface, subTextile makes it possible for novices to explore electronic textiles without concern for technical details. This thesis presents the design considerations and motivations that drove the creation of subTextile. Also presented are the result of a preliminary evaluation of the language, done with a sample chosen to represent users with varying capabilities in both the technical and design domains.by Sajid H. Sadi.S.M

Interacción Natural Basada en un Conjunto Mínimo de Sensores Inerciales para Realidad Virtual sin Cables

La Realidad Virtual tiene un enorme potencial aún por explotar. Esta tesis doctoral pretende ir un paso más allá en el desarrollo de sistemas de Realidad Virtual inmersivos. En concreto, su objetivo fundamental es diseñar, desarrollar y evaluar una plataforma experimental sin cables para investigación en Realidad Virtual inmersiva con navegación natural e interacción manual basada en un conjunto mínimo de sensores inerciales. Para ello se emplea metodología científica desde la perspectiva de la interacción persona computador (Human Computer Interaction, HCI). A partir del objetivo fundamental, se elaboran las recomendaciones de diseño y especificaciones del sistema a desarrollar. Tras revisar en detalle el estado del arte y establecer el planteamiento metodológico, comienza el desarrollo de herramientas en las que se basará la creación de prototipos. Durante la tesis doctoral se desarrollan 3 herramientas de investigación y 5 prototipos que se evalúan a través de diversas pruebas con usuarios y 2 experimentos. En total, participan generosamente más de 85 personas. El desarrollo de prototipos da lugar a técnicas específicas que resultan de interés por sí mismas para la comunidad científica. Por otra parte, los experimentos también aportan resultados susceptibles de ser divulgados. Uno de los experimentos realizados permite evaluar las técnicas desarrolladas para implementar un sistema de Realidad Virtual con navegación natural. El otro experimento, estudia el comportamiento del sistema de tracking para interacción manual desarrollado durante el proyecto de investigación. Además, utiliza una televisión 3D y el casco de Realidad Virtual Oculus Rift para realizar un estudio comparativo de diversos aspectos como el rendimiento, usabilidad, nivel de presencia, dificultad y preferencia. El proyecto de investigación asociado a esta tesis doctoral da lugar a varias aportaciones de distinta naturaleza como publicaciones científicas, herramientas de investigación, algoritmos y trazas de datos, además de la propia plataforma experimental que permitirá abordar nuevos estudios de Realidad Virtual inmersiva con navegación natural e interacción manual