Author : Wolfgang Teufl
Publisher :
Page : pages
File Size : 34,22 MB
Release : 2021
Category : Motion
ISBN :
Author : William Frey
Publisher :
Page : 170 pages
File Size : 17,42 MB
Release : 1996-09-01
Category :
ISBN : 9781423574651
Human body tracking for synthetic environment interface has become a significant human- computer interface challenge. There are several different types of motion capture systems currently available. Inherent problems, most resulting from the use of artificially-generated source signals, plague these systems. A proposed motion capture system is being developed at the Naval Postgraduate School which utilizes a combination of inertial sensors to overcome these difficulties. However, the current design exhibits azimuth drift errors resulting from the use of inertial sensors. This thesis proposes a new method of compensating for azimuth drift using a three-axis fluxgate magnetometer. The magnetometer capable of azimuth drift compensation since its sensitive axis is not collinear with the local vertical. This thesis includes a program for simulating the operation of a fluxgate magnetometer in C++. The included C++ code simulates a fluxgate magnetometer and provides an estimate of azimuth based on the magnetometer's output which is typically within five degrees of the actual azimuth. Real magnetometer data for testing and verification was accomplished by bench testing a real fluxgate magnetometer.
Author : Daniel Tze Huei Lai
Publisher : CRC Press
Page : 464 pages
File Size : 15,28 MB
Release : 2016-04-19
Category : Medical
ISBN : 1000755703
Healthcare sensor networks (HSNs) now offer the possibility to continuously monitor human activity and physiological signals in a mobile environment. Such sensor networks may be able to reduce the strain on the present healthcare workforce by providing new autonomous monitoring services ranging from simple user-reminder systems to more advanced mon
Author : Matteo Zago
Publisher : Frontiers Media SA
Page : 328 pages
File Size : 50,23 MB
Release : 2021-03-04
Category : Science
ISBN : 2889665615
Author : Chen, Xi (Researcher on human mechanics)
Publisher :
Page : 169 pages
File Size : 19,83 MB
Release : 2013
Category : Human locomotion
ISBN :
High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson's disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user's itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system.
Author : Jung Keun Lee
Publisher :
Page : 0 pages
File Size : 50,61 MB
Release : 2010
Category : Accelerometers
ISBN :
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 : Yatiraj K Shetty
Publisher :
Page : 136 pages
File Size : 31,53 MB
Release : 2016
Category : Human mechanics
ISBN :
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 : Gaurav Jagdish Ghorpade
Publisher :
Page : 94 pages
File Size : 22,23 MB
Release : 2017
Category :
ISBN :
This study aims to evaluate an inertial measurement unit (IMU)-based motion capture system for lower body gait analysis on treadmills in terms of its accuracy, repeatability and sensitivity. The Xsens MVN BIOMECH is a popular inertial sensor-based motion capture system widely used by the gait community. However, there is insufficient information regarding its validation for use in gait. Accuracy of pelvic and lower body segments was evaluated with respect to a PhaseSpace Motion Capture System with a thirteen-camera setup. RMS errors for joint angles were evaluated at gait speeds of 1 m/s and 0.5 m/s. Repeatability was evaluated using the Coefficient of Multiple Correlation (CMC) on two different days. Sensitivity of the IMU-based motion capture (mo-cap) system was analyzed based on its ability to distinguish between gait for 1 m/s (symmetric) and 0.95 m/s, 0.9 m/s, 0.85 m/s and 0.8 m/s (asymmetric) conditions. Data from 10 healthy, young individuals were collected and analyzed. We found that the IMU-based system demonstrates reasonably high accuracy when measuring joint angles (0.47 to 3.9 degrees). Accuracy was affected by speed with higher accuracy at lower speeds, especially for Ankle dorsi/plantarflexion. Repeatability was established, with high CMC values (0.76 to 0.98), lower than similar previous studies. Gait cycles were treated as coherent entities and the ability to distinguish between small changes (0.05 m/s) was demonstrated. Sensitivity of gait cycles were compared using High Dimensional Analysis of Variance and the Adaptive Neyman test both for groups and individuals. Smaller differences can be detected at the individual level since gait can vary considerably across individuals. A representative case demonstrated significant differences (p
Author : Eric Robert Bachmann
Publisher :
Page : 199 pages
File Size : 47,35 MB
Release : 2000-12-01
Category :
ISBN : 9781423532248
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 : Shaghayegh Zihajehzadeh
Publisher :
Page : 115 pages
File Size : 49,72 MB
Release : 2017
Category :
ISBN :
Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturing process of MEMS sensors, existing wearable MoCap systems suffer from drift error and accuracy degradation over time caused by time-varying bias. The goal of this research is to develop algorithms based on multi-sensor fusion and machine learning techniques for precise tracking of human motion and location using wearable inertial sensors integrated with absolute localization technologies. The main focus of this research is on true ambulatory applications in active sports (e.g., skiing) and entertainment (e.g., gaming and filmmaking), and health-status monitoring. For active sports and entertainment applications, a novel sensor fusion algorithm is developed to fuse inertial data with magnetic field information and provide drift-free estimation of human body segment orientation. This concept is further extended to provide ubiquitous indoor/outdoor localization by fusing wearable inertial/magnetic sensors with global navigation satellite system (GNSS), barometric pressure sensor and ultra-wideband (UWB) localization technology. For health applications, this research is focused on longitudinal tracking of walking speed as a fundamental indicator of human well-being. A regression model is developed to map inertial information from a single waist or ankle-worn sensor to walking speed. This approach is further developed to estimate walking speed using a wrist-worn device (e.g., a smartwatch) by extracting the arm swing motion intensity and frequency by combining sensor fusion and principal component analysis.
