Author: Ami Luttwak
Publisher:
ISBN:
Category :
Languages : en
Pages : 90

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Author: Manon Kok
Publisher:
ISBN: 9781680833577
Category : Electronic books
Languages : en
Pages : 153

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In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors.We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

Author: Jung Keun Lee
Publisher:
ISBN:
Category : Accelerometers
Languages : en
Pages : 0

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Recent advances in miniature sensors and mobile computing have fostered a dramatic growth of interest for 'ambulatory' human motion tracking. Inertial (i.e. accelerometers and gyroscopes) and magnetic sensors do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. This thesis presents ambulatory human motion tracking using inertial/magnetic sensing. In particular, the purpose of this thesis is to introduce novel orientation estimation algorithms using an inertial/magnetic sensor and demonstrate practical applications of the inertial/magnetic sensors in spinal and gait analysis. First, two quaternion-based orientation estimation algorithms were newly developed with focus on improving computational efficiency. Both algorithms deal with so-called Wahba's problem, a least squares minimization problem, to find a best fit orientation estimation solution. A major difference between them is that one is based on a deterministic approach using a Gauss-Newton method and the other is based on a stochastic approach that employs Kalman filtering. The Gauss-Newton method in the former was formulated using virtual rotation concept while the Kalman filter in the latter was designed to have a minimum-order structure, which significantly improves the computational efficiency of each algorithm. Second, a novel 3D spinal motion measurement system based on inertial/magnetic sensors was proposed. The proposed system can provide not only 3D orientations of the spine/pelvis but also temporal gait parameters, enabling a comprehensive analysis of the 3D spinal kinematics together with the gait analysis. In particular, the spinal motions during the staircase walking were compared to those during level walking using the proposed system, to fill a gap in the spinal kinematics literature. Furthermore, the system was applied to investigate low back pain effects on spinal motion during stair-climbing. This study revealed that the lumbar spinal sagittal motion during stair-climbing can provide an effective quantitative measure in the assessment of low back pain patients. In addition to the spinal motion analysis, an automatic gait event detection algorithm using shank attached inertial sensors was presented for further gait analysis. The outcomes of the research in this thesis can serve as foundation towards achieving a truly ambulatory human motion tracking system.

Author:
Publisher:
ISBN:
Category : Human mechanics
Languages : en
Pages : 75

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In the past several years, IMU's have been widely used to measure the orientation of a moving body over a continuous period of time. Although, inertial navigation is a common approach for estimating the orientation, it greatly suffers from the accumulation of error in the orientation estimation. Most of the current common practices apply zero velocity update as a calibration method to address this problem and improve the estimation accuracy. However, this approach requires the sensors to be stationary frequently. This thesis introduces a novel method of calibration for estimating the elevation and bank angles of the orientation over a persistent human movement utilizing accelerometers and gyroscopes. The proposed technique incorporates the prior knowledge about the human motion to the estimation of the orientation to prevent the estimated position from growing unboundedly. The measurement model is designed to estimate the position for T seconds in the future. The knowledge of the estimated position for few seconds further in the future provides a feedback for orientation estimation during the periods of time when the accelerometer's readings are significantly deviated from gravity. This work evaluates the performance of the proposed method in two different ways: 1. a model of human movement is designed to generate synthetic data which resembles human motion. 2. an experimental design is implemented using a robot arm and an actual IMU to capture real data. The performance of the new technique is compared with the results from the inertial navigation approach. It is demonstrated that the new method significantly improves the accuracy of the orientation estimation.

Author: Paul Kisik Yoon
Publisher:
ISBN:
Category :
Languages : en
Pages : 62

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Recently, miniature inertial measurement units (IMUs) have been deployed as wearable devices to monitor human motion in an ambulatory fashion. This thesis presents a robust human motion tracking algorithm using the IMU and radio-based wireless sensors, such as the Bluetooth Low Energy (BLE) and ultra-wideband (UWB). First, a novel indoor localization method using the BLE and IMU is proposed. The BLE trilateration residue is deployed to adaptively weight the estimates from these sensor modalities. Second, a robust sensor fusion algorithm is developed to accurately track the location and capture the lower body motion by integrating the estimates from the UWB system and IMUs, but also taking advantage of the estimated height and velocity obtained from an aiding lower body biomechanical model. The experimental results show that the proposed algorithms can maintain high accuracy for tracking the location of a sensor/subject in the presence of the BLE/UWB outliers and signal outages.

Author: Yatiraj K Shetty
Publisher:
ISBN:
Category : Human mechanics
Languages : en
Pages : 136

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The advancements in the technology of MEMS fabrication has been phenomenal in recent years. In no mean measure this has been the result of continued demand from the consumer electronics market to make devices smaller and better. MEMS inertial measuring units (IMUs) have found revolutionary applications in a wide array of fields like medical instrumentation, navigation, attitude stabilization and virtual reality. It has to be noted though that for advanced applications of motion tracking, navigation and guidance the cost of the IMUs is still pretty high. This is mainly because the process of calibration and signal processing used to get highly stable results from MEMS IMU is an expensive and time-consuming process. Also to be noted is the inevitability of using external sensors like GPS or camera for aiding the IMU data due to the error propagation in IMU measurements adds to the complexity of the system.First an efficient technique is proposed to acquire clean and stable data from unaided IMU measurements and then proceed to use that system for tracking human motion. First part of this report details the design and development of the low-cost inertial measuring system yIMU. This thesis intends to bring together seemingly independent techniques that were highly application specific into one monolithic algorithm that is computationally efficient for generating reliable orientation estimates. Second part, systematically deals with development of a tracking routine for human limb movements. The validity of the system has then been verified.The central idea is that in most cases the use of expensive MEMS IMUs is not warranted if robust smart algorithms can be deployed to gather data at a fraction of the cost. A low-cost prototype has been developed comparable to tactical grade performance for under $15 hardware. In order to further the practicability of this device we have applied it to human motion tracking with excellent results. The commerciality of device has hence been thoroughly established.

Author: Ya Tian
Publisher:
ISBN:
Category :
Languages : en
Pages : 133

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This thesis comprises three specific goals using our developed IMU board and the camera from the imaging source company: (1) to develop a robust and real-time orientation algorithm using only the measurements from IMU; (2) to develop a robust distance estimation in static free-living environments to estimate people's position and navigate people in static free-living environments and simultaneously the scale ambiguity problem, usually appearing in the monocular camera tracking, is solved by integrating the data from the visual and inertial sensors; (3) in case of moving objects viewed by the camera existing in free-living environments, to firstly design a robust scene segmentation algorithm and then respectively estimate the motion of the vIMU system and moving objects.

Author: Hua-I Chang
Publisher:
ISBN:
Category :
Languages : en
Pages : 117

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Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Video based motion capturing systems (e.g., VICON cameras) provide a partial solution. However, these expensive and fixed systems are not suitable for patients' at-home daily motion monitoring. Wireless motion sensors, including accelerometers and gyroscopes, can provide a low-cost, small-size, and highly-mobile option. However, acquiring robust inference of human motion trajectory via low-cost inertial sensors remains challenging. Sensor noise and drift, sensor placement errors and variation of activity over the population all lead to the necessity of a large amount of data collection. Unfortunately, such a large amount of data collection is prohibitively costly. In observance of these issues, a series of solutions for robust human motion monitoring and activity classification will be presented. The implementation of a real-time context-guided activity classification system will be discussed. To facilitate ground truth data acquisition, we proposed a virtual inertial measurements platform to convert the currently available MoCap database into a noiseless and error-free inertial measurements database. An opportunistic calibration system which deals with sensor placement errors will be discussed. In addition, a sensor fusion approach for robust upper limb motion tracking will also be presented.

Author: Eric Robert Bachmann
Publisher:
ISBN: 9781423532248
Category :
Languages : en
Pages : 199

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Current motion tracking technologies fail to provide accurate wide area tracking of multiple users without interference and occlusion problems. This research proposes to overcome current limitations using nine-axis magnetic/ angular/rate/gravity (MARG) sensors combined with a quaternion-based complementary filter algorithm capable of continuously correcting for drift and following angular motion through all orientations without singularities. Primarily, this research involves the development of a prototype tracking system to demonstrate the feasibility of MARG sensor body motion tracking Mathematical analysis and computer simulation are used to validate the correctness of the complementary filter algorithm The implemented human body model utilizes the world-coordinate reference frame orientation data provided in quaternion form by the complementary filter and orients each limb segment independently. Calibration of the model and the inertial sensors is accomplished using simple but effective algorithms. Physical experiments demonstrate the utility of the proposed system by tracking of human limbs in real-time using multiple MARG sensors. The system is "sourceless" and does not suffer from range restrictions and interference problems. This new technology overcomes the limitations of motion tracking technologies currently in use. It has the potential to provide wide area tracking of multiple users in virtual environment and augmented reality applications.

Author: Hannes Ovrén
Publisher: Linköping University Electronic Press
ISBN: 917685244X
Category :
Languages : en
Pages : 67

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Using images to reconstruct the world in three dimensions is a classical computer vision task. Some examples of applications where this is useful are autonomous mapping and navigation, urban planning, and special effects in movies. One common approach to 3D reconstruction is ”structure from motion” where a scene is imaged multiple times from different positions, e.g. by moving the camera. However, in a twist of irony, many structure from motion methods work best when the camera is stationary while the image is captured. This is because the motion of the camera can cause distortions in the image that lead to worse image measurements, and thus a worse reconstruction. One such distortion common to all cameras is motion blur, while another is connected to the use of an electronic rolling shutter. Instead of capturing all pixels of the image at once, a camera with a rolling shutter captures the image row by row. If the camera is moving while the image is captured the rolling shutter causes non-rigid distortions in the image that, unless handled, can severely impact the reconstruction quality. This thesis studies methods to robustly perform 3D reconstruction in the case of a moving camera. To do so, the proposed methods make use of an inertial measurement unit (IMU). The IMU measures the angular velocities and linear accelerations of the camera, and these can be used to estimate the trajectory of the camera over time. Knowledge of the camera motion can then be used to correct for the distortions caused by the rolling shutter. Another benefit of an IMU is that it can provide measurements also in situations when a camera can not, e.g. because of excessive motion blur, or absence of scene structure. To use a camera together with an IMU, the camera-IMU system must be jointly calibrated. The relationship between their respective coordinate frames need to be established, and their timings need to be synchronized. This thesis shows how to automatically perform this calibration and synchronization, without requiring e.g. calibration objects or special motion patterns. In standard structure from motion, the camera trajectory is modeled as discrete poses, with one pose per image. Switching instead to a formulation with a continuous-time camera trajectory provides a natural way to handle rolling shutter distortions, and also to incorporate inertial measurements. To model the continuous-time trajectory, many authors have used splines. The ability for a spline-based trajectory to model the real motion depends on the density of its spline knots. Choosing a too smooth spline results in approximation errors. This thesis proposes a method to estimate the spline approximation error, and use it to better balance camera and IMU measurements, when used in a sensor fusion framework. Also proposed is a way to automatically decide how dense the spline needs to be to achieve a good reconstruction. Another approach to reconstruct a 3D scene is to use a camera that directly measures depth. Some depth cameras, like the well-known Microsoft Kinect, are susceptible to the same rolling shutter effects as normal cameras. This thesis quantifies the effect of the rolling shutter distortion on 3D reconstruction, depending on the amount of motion. It is also shown that a better 3D model is obtained if the depth images are corrected using inertial measurements. Att använda bilder för att återskapa världen omkring oss i tre dimensioner är ett klassiskt problem inom datorseende. Några exempel på användningsområden är inom navigering och kartering för autonoma system, stadsplanering och specialeffekter för film och spel. En vanlig metod för 3D-rekonstruktion är det som kallas ”struktur från rörelse”. Namnet kommer sig av att man avbildar (fotograferar) en miljö från flera olika platser, till exempel genom att flytta kameran. Det är därför något ironiskt att många struktur-från-rörelse-algoritmer får problem om kameran inte är stilla när bilderna tas, exempelvis genom att använda sig av ett stativ. Anledningen är att en kamera i rörelse ger upphov till störningar i bilden vilket ger sämre bildmätningar, och därmed en sämre 3D-rekonstruktion. Ett välkänt exempel är rörelseoskärpa, medan ett annat är kopplat till användandet av en elektronisk rullande slutare. I en kamera med rullande slutare avbildas inte alla pixlar i bilden samtidigt, utan istället rad för rad. Om kameran rör på sig medan bilden tas uppstår därför störningar i bilden som måste tas om hand om för att få en bra rekonstruktion. Den här avhandlingen berör robusta metoder för 3D-rekonstruktion med rörliga kameror. En röd tråd inom arbetet är användandet av en tröghetssensor (IMU). En IMU mäter vinkelhastigheter och accelerationer, och dessa mätningar kan användas för att bestämma hur kameran har rört sig över tid. Kunskap om kamerans rörelse ger möjlighet att korrigera för störningar på grund av den rullande slutaren. Ytterligare en fördel med en IMU är att den ger mätningar även i de fall då en kamera inte kan göra det. Exempel på sådana fall är vid extrem rörelseoskärpa, starkt motljus, eller om det saknas struktur i bilden. Om man vill använda en kamera tillsammans med en IMU så måste dessa kalibreras och synkroniseras: relationen mellan deras respektive koordinatsystem måste bestämmas, och de måste vara överens om vad klockan är. I den här avhandlingen presenteras en metod för att automatiskt kalibrera och synkronisera ett kamera-IMU-system utan krav på exempelvis kalibreringsobjekt eller speciella rörelsemönster. I klassisk struktur från rörelse representeras kamerans rörelse av att varje bild beskrivs med en kamera-pose. Om man istället representerar kamerarörelsen som en tidskontinuerlig trajektoria kan man på ett naturligt sätt hantera problematiken kring rullande slutare. Det gör det också enkelt att införa tröghetsmätningar från en IMU. En tidskontinuerlig kameratrajektoria kan skapas på flera sätt, men en vanlig metod är att använda sig av så kallade splines. Förmågan hos en spline att representera den faktiska kamerarörelsen beror på hur tätt dess knutar placeras. Den här avhandlingen presenterar en metod för att uppskatta det approximationsfel som uppkommer vid valet av en för gles spline. Det uppskattade approximationsfelet kan sedan användas för att balansera mätningar från kameran och IMU:n när dessa används för sensorfusion. Avhandlingen innehåller också en metod för att bestämma hur tät en spline behöver vara för att ge ett gott resultat. En annan metod för 3D-rekonstruktion är att använda en kamera som också mäter djup, eller avstånd. Vissa djupkameror, till exempel Microsoft Kinect, har samma problematik med rullande slutare som vanliga kameror. I den här avhandlingen visas hur den rullande slutaren i kombination med olika typer och storlekar av rörelser påverkar den återskapade 3D-modellen. Genom att använda tröghetsmätningar från en IMU kan djupbilderna korrigeras, vilket visar sig ge en bättre 3D-modell.