Understanding the motion of a camera from only the image(s) it captures is a di cult problem. At best we might hope to estimate the relative motion between camera and scene if we assume a static subject, but once we start considering scenes with dynamic content it becomes di cult to di↵erentiate between motion due to the observer or motion due to scene movement. In this thesis we show how the invaluable cues provided by inertial sensor data can be used to simplify motion analysis and relax requirements for several computer vision problems. This work was funded by the University of Bath.

A marcha humana, ou caminhada, é um padrão cíclico de movimentos corporais que se repetem a cada passo que desloca um indivíduo de um local a outro. Atualmente, avaliações biomecânicas da marcha humana tem sido utilizado no diagnóstico de alterações neuromusculares, músculo-esqueléticas e como forma de avaliação pré e pós-tratamento cirúrgico, medicamentoso e/ou fisioterapêutico. O presente trabalho apresenta o desenvolvimento de uma ferramenta acadêmica de baixo custo para o estudo da marcha humana. Esse sistema consiste no sensoriamento da marcha de um usuário através de sensores inerciais e de um modelo virtual do corpo humano para permitir a visualização do movimento gerado. Dessa maneira o usuário poderá ter suas ações corrigidas por sua percepção visual e também corrigida pelas orientações de um fisiatra ou fisioterapeuta que terá a reprodução do modelo virtual conforme a movimentação detalhada do paciente para análise. O sistema ainda efetuará os registros das variáveis cinemáticas da marcha (tais como aceleração, velocidade angular, angulações dos membros sensoriados) para estudos e acompanhamento mais detalhado da sua recuperação e/ou tratamento. Como resultado, o sistema desenvolvido obteve erros médios de X 0,52º Y 1,20º Z 1,80º e erros em RMS de X 3,01º Y 3,30º Z 5,70º quando comparados com um sistema comercial, sendo esse resultado próximo à literatura e aplicável em exames biomecânicos de marcha. / The human gait is a cyclical pattern of body movements that are repeated every step that moves a subject from one location to another. Currently, biomechanical assessments of human gait has been used for diagnosing neuromuscular disorders, musculoskeletal and as a way of pre and post-surgical treatment, medication and/or physical therapy. This paper presents the development of a low cost academic tool for the study of human gait. This system consists of sensing the motion of a user through inertial sensors and a virtual model of the human body to allow the visualization of the generated movement. In this way, the user can have its actions corrected by his visual perception and also corrected by therapist or physiotherapist who will visualize the virtual model as the detailed movements of patient. The system will also record the kinematic gait variables (as acceleration, angular velocity, angles of the sensed members) for studies and more detailed monitoring of their recovery and/or treatment. As result, the developed system obtained average errors of X 0,52º Y 1,20º Z 1,80º and errors in RMS X 3,01º Y 3,30º Z 5,70º compared to a commercial system, and these results close to the ones seen in literature and applicable in biomechanical tests of gait.

This work reports the use of a head-motion monitoring system to record patient head movements while completing in-home exercises for vestibular rehabilitation therapy. Based upon a dual-axis gyroscope (yaw and pitch, ± 500-degrees/sec maximum), angular head rotations were measured and stored via an on-board memory card. The system enabled the clinician to document exercises at home. Several measurements were recorded in one patient with unilateral vestibular hypofunction: The total time of exercise for the week (118 minutes) was documented and compared with expected weekly exercise time (140 minutes). For gaze stabilization exercises, execution time of 60 sec was expected, and observed times ranged from 75-100 sec. An absence of rest periods between each exercise instead of the recommended one minute rest period was observed. Maximum yaw head velocities from approximately 100-350 degrees/sec were detected. A second subject provided feedback concerning the ease of use of the HAMMS device. This pilot study demonstrates, for the first time, the capability to capture the head-motion “signature” of a patient while completing vestibular rehabilitation exercises in the home and to extract exercise regime parameters and monitor patient adherence. This emerging technology has the potential to greatly improve rehabilitation outcomes for individuals completing in-home gaze stabilization exercises 1 .

As a promising alternative to laboratory constrained video capture systems in studies of human movement, inertial sensors (accelerometers and gyroscopes) are recently gaining popularity. Secondary quantities such as velocity, displacement and joint angles can be calculated through integration of acceleration and angular velocities. However, it is broadly accepted that this procedure is significantly influenced by cumulative errors due to integration, arising from sensor noise, non-linearities, asymmetries, sensitivity variations and bias drifts. In this study, new methods for improving foot motion from inertial sensors are explored and assessed. / Sensor devices have been developed previously, for example, to detect postural changes that determine potential elderly fallers, and monitor a person’s gait. Recently, a gait variable known as minimum toe clearance (MTC) has been proposed to describe age-related declines in gait with better success as a predictor of falls risk. The MTC is the minimum vertical distance between the lowest point on the shoe and the ground during the mid-swing phase of the gait cycle. It is therefore of our interest to design a cost effective but accurate solution to measure toe clearance data which can then be used to identify the individuals at risk of falling. In this study, hardware, firmware and software features from off-the-shelf inertial sensors and wireless motes are evaluated and their configuration optimized for this application. A strap-down method, which consists of the minimizing of the integration drift due to cumulative errors, is evaluated off-line. Analysis revealed the necessity of band-pass filtering methods to correct systematic sensor errors that dramatically reduce the accuracy in estimating foot motion. / Cumulative errors were studied in the frequency domain, employing content of inertial sensor foot motion evaluated against a ’gold standard’ video-based device, namely the Optotrak Certus NDI. In addition, the effectiveness of applying band-pass filtering to raw inertial sensor data is assessed, under the assumption that sensor drift errors occur in the low frequency spectrum. The normalized correlation coefficient ρ of the Fast Fourier Transform (FFT) spectra corresponding to vertical toe acceleration from inertial sensors and from a video capture system as a function of digital band-pass filter parameters is compared. The Root Mean Square Error (RMSE) of the vertical toe displacement is calculated for 5 healthy subjects over a range of 4 walking speeds. The lowest RMSE and highest cross correlation achieved for the slowest walking speed of 2.5km/h was 3.06cmand 0.871 respectively, and 2.96cm and 0.952 for the fastest speed of 5.5km/h.

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.

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.

Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477

Global position system (GPS) is widely used in land vehicles but suffers deterioration in its accuracy in urban canyons; mostly due to satellite signal blockage and signal multipath. To obtain accurate, reliable, and continuous positioning solutions, GPS is usually augmented with inertial sensors, including accelerometers and gyroscopes to monitor both translational and rotational motions of a moving vehicle. Due to space and cost requirements, micro-electro-mechanical-system (MEMS) inertial sensors, which are typically inexpensive are presently utilized in land vehicles for various reasons and can be used for integration with GPS for navigation purposes. Kalman filtering (KF) usually used to performs this integration. However, the complex error characteristics of these MEMS based sensors lead to divergence of the positioning solution. Furthermore, the residual GPS pseudorange correlated errors are always ignored, thus reducing the GPS overall positioning accuracy. This thesis targets enhancing the performance of integrated MEMS based INS/GPS navigation systems through exploring new non-linear modelling approaches that can deal with the non-linear and correlated parts of INS and GPS errors. The research approach in this thesis relies on reduced inertial sensor systems (RISS) incorporating single axis gyroscope, vehicle odometer, and accelerometers is considered for the integration with GPS in one of two schemes; either loosely-coupled where GPS position and velocity are used for the integration or tightly-coupled where GPS pseudorange and pseudorange rates are utilized. A new method based on parallel cascade identification (PCI) is developed in this research to enhance the performance of KF by modelling azimuth errors for the RISS/GPS loosely-coupled integration scheme. In addition, PCI is also utilized for the modelling of residual GPS pseudorange correlated errors. This thesis develops a method to augment a PCI – based model of GPS pseudorange correlated errors to a tightly-coupled KF. In order to take full advantage of the PCI based models, this thesis explores the Particle filter (PF) as a non-linear integration scheme that is capable of accommodating the arbitrary sensor characteristics, motion dynamics, and noise distributions. The performance of the proposed methods is examined through several road test experiments in land vehicles involving different types of inertial sensors and GPS receivers. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-07-31 16:09:16.559

The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of MEMS technology. As a result of this trend, inertial sensors have become commonplace for many applications and can even be found in many consumer products, for instance smart phones, cameras and game consoles. Due to the drift inherent in inertial technology, inertial sensors are typically used in combination with aiding sensors to stabilize andimprove the estimates. The need for aiding sensors becomes even more apparent due to the reduced accuracy of MEMS inertial sensors. This thesis discusses two problems related to using inertial sensors in combination with aiding sensors. The first is the problem of sensor fusion: how to combine the information obtained from the different sensors and obtain a good estimate of position and orientation. The second problem, a prerequisite for sensor fusion, is that of calibration: the sensors themselves have to be calibrated and provide measurement in known units. Furthermore, whenever multiple sensors are combined additional calibration issues arise, since the measurements are seldom acquired in the same physical location and expressed in a common coordinate frame. Sensor fusion and calibration are discussed for the combination of inertial sensors with cameras, UWB or GPS. Two setups for estimating position and orientation in real-time are presented in this thesis. The first uses inertial sensors in combination with a camera; the second combines inertial sensors with UWB. Tightly coupled sensor fusion algorithms and experiments with performance evaluation are provided. Furthermore, this thesis contains ideas on using an optimization based sensor fusion method for a multi-segment inertial tracking system used for human motion capture as well as a sensor fusion method for combining inertial sensors with a dual GPS receiver. The above sensor fusion applications give rise to a number of calibration problems. Novel and easy-to-use calibration algorithms have been developed and tested to determine the following parameters: the magnetic field distortion when an IMU containing magnetometers is mounted close to a ferro-magnetic object, the relative position and orientation of a rigidly connected camera and IMU, as well as the clock parameters and receiver positions of an indoor UWB positioning system. / MATRIS (Markerless real-time Tracking for Augmented Reality Image), a sixth framework programme funded by the European Union / CADICS (Control, Autonomy, and Decision-making in Complex Systems), a Linneaus Center funded by the Swedish Research Council (VR) / Strategic Research Center MOVIII, funded by the Swedish Foundation for Strategic Research (SSF)

The use of MEMS-based inertial sensors for a relative positioning estimation system (RPES) was investigated. A number of data acquisition and processing techniques are developed and tested, to determine which one would provide the best performance of the proposed method. Because inertial-based sensors don’t rely on other references to calibrate their position and orientation, there is a steady accumulation of errors over time. The errors are caused by various sources of noise such as temperature and vibration, and the errors are significant. This work investigates various methods to increase the signalto- noise ratio, in order to develop the best possible RPES method. The main areas of this work are as follows: (i) The proposed RPES application imposes specific boundary conditions to the signal processing, to increase the accuracy. (ii) We propose that using redundant inertial rate sensors would give a better performance over a single rate sensor. (iii) We investigate three Kalman filter algorithms to accommodate different combinations of sensors: Parallel sensors arrangement, Series sensors arrangement, and compression arrangement. In implementing these three areas, the results show that there is much better improvement in the data in comparison to using regular averaging techniques. / Graduate