3,161 research outputs found
Development of Kinectแตแดฟ applications for assembly simulation and ergonomic analysis
Marker-less motion capture technology has been harnessed for several years to track human movements for developing various applications. Recently, with the launch of Microsoft Kinect, researchers have been keenly interested in developing applications using this device. Since Kinect is very inexpensive (only $110 at the time of writing this thesis), it is a low-cost and a promising substitute for the comparatively expensive marker-based motion capture systems. Though it is principally designed for home entertainment, numerous applications can be developed with the capabilities of Kinect. The skeleton data of a human being tracked by a single Kinect device is enough to simulate the human movements, in some cases. However, it is highly desirable to develop a multiple Kinect system to enhance the tracking volume and to address an issue of occlusions. This thesis presents a novel approach for addressing the issue of interference of infrared light patterns while using multiple Kinect devices for human motion capture without lowering the frame rate. This research also presents a software solution to obtain skeleton data from multiple Kinect devices using Kinect for Windows SDK. It also discusses the development of an application involving auto scaling of a human model in digital human modeling software by Siemens Jack and human motion simulation using skeleton tracking data from Kinect to assist the industries with a flexible tool for ergonomic analysis. Further, the capability of this application for obtaining assembly simulations of fastening operations on an aircraft fuselage is also presented. --Abstract, page iii
Acquisition and distribution of synergistic reactive control skills
Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots
This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions.
Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning
Structured manifolds for motion production and segmentation : a structured Kernel Regression approach
Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010
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Tracking and modelling motion for biomechanical analysis
This thesis focuses on the problem of determining appropriate skeletal configurations for which a virtual animated character moves to desired positions as smoothly, rapidly, and as accurately as possible. During the last decades, several methods and techniques, sophisticated or heuristic, have been presented to produce smooth and natural solutions to the Inverse Kinematics (IK) problem. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this study, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is proposed, which returns
visually natural poses in real-time, equally comparable with highly sophisticated approaches. It is capable of supporting constraints for most of the known joint types and it can be extended to solve problems with multiple end effectors, multiple targets and closed loops. FABRIK was
compared against the most popular IK approaches and evaluated in terms of its robustness and performance limitations. This thesis also includes a robust methodology for marker prediction under multiple marker occlusion for extended time periods, in order to drive real-time centre of rotation (CoR) estimations. Inferred information from neighbouring markers has been utilised, assuming that the inter-marker distances remain constant over time. This is the first
time where the useful information about the missing markers positions which are partially visible to a single camera is deployed. Experiments demonstrate that the proposed methodology can effectively track the occluded markers with high accuracy, even if the occlusion persists for extended periods of time, recovering in real-time good estimates of the true joint positions.
In addition, the predicted positions of the joints were further improved by employing FABRIK to relocate their positions and ensure a fixed bone length over time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions. Finally, an efficient model for real-time hand tracking and reconstruction that requires a minimum
number of available markers, one on each finger, is presented. The proposed hand model
is highly constrained with joint rotational and orientational constraints, restricting the fingers and palm movements to an appropriate feasible set. FABRIK is then incorporated to estimate the remaining joint positions and to fit them to the hand model. Physiological constraints, such as inertia, abduction, flexion etc, are also incorporated to correct the final hand posture. A mesh deformation algorithm is then applied to visualise the movements of the underlying hand skeleton for comparison with the true hand poses. The mathematical framework used for describing and implementing the techniques discussed within this thesis is Conformal Geometric
Algebra (CGA)
Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data
In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrapand k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead tocorrect classification rates with less than 10% of the original features
Human Motion Analysis with Wearable Inertial Sensors
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 Parkinsonโs 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 userโs itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system
์ฌ์ธต ๊ฐํํ์ต์ ์ด์ฉํ ์ฌ๋์ ๋ชจ์ ์ ํตํ ์ดํ์ ์บ๋ฆญํฐ ์ ์ด๊ธฐ ๊ฐ๋ฐ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2022. 8. ์์ง์ฑ.์ฌ๋์ ๋ชจ์
์ ์ด์ฉํ ๋ก๋ด ์ปจํธ๋กค ์ธํฐํ์ด์ค๋ ์ฌ์ฉ์์ ์ง๊ด๊ณผ ๋ก๋ด์ ๋ชจํฐ ๋ฅ๋ ฅ์ ํฉํ์ฌ ์ํํ ํ๊ฒฝ์์ ๋ก๋ด์ ์ ์ฐํ ์๋์ ๋ง๋ค์ด๋ธ๋ค. ํ์ง๋ง, ํด๋จธ๋
ธ์ด๋ ์ธ์ ์ฌ์กฑ๋ณดํ ๋ก๋ด์ด๋ ์ก์กฑ๋ณดํ ๋ก๋ด์ ์ํ ๋ชจ์
์ธํฐํ์ด์ค๋ฅผ ๋์์ธ ํ๋ ๊ฒ์ ์ฌ์ด์ผ์ด ์๋๋ค. ์ด๊ฒ์ ์ฌ๋๊ณผ ๋ก๋ด ์ฌ์ด์ ํํ ์ฐจ์ด๋ก ์ค๋ ๋ค์ด๋๋ฏน์ค ์ฐจ์ด์ ์ ์ด ์ ๋ต์ด ํฌ๊ฒ ์ฐจ์ด๋๊ธฐ ๋๋ฌธ์ด๋ค. ์ฐ๋ฆฌ๋ ์ฌ๋ ์ฌ์ฉ์๊ฐ ์์ง์์ ํตํ์ฌ ์ฌ์กฑ๋ณดํ ๋ก๋ด์์ ๋ถ๋๋ฝ๊ฒ ์ฌ๋ฌ ๊ณผ์ ๋ฅผ ์ํํ ์ ์๊ฒ๋ ํ๋ ์๋ก์ด ๋ชจ์
์ ์ด ์์คํ
์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ์ฐ์ ์บก์ณํ ์ฌ๋์ ๋ชจ์
์ ์์ํ๋ ๋ก๋ด์ ๋ชจ์
์ผ๋ก ๋ฆฌํ๊ฒ ์ํจ๋ค. ์ด๋ ์์ํ๋ ๋ก๋ด์ ๋ชจ์
์ ์ ์ ๊ฐ ์๋ํ ์๋ฏธ๋ฅผ ๋ดํฌํ๊ฒ ๋๋ฉฐ, ์ฐ๋ฆฌ๋ ์ด๋ฅผ ์ง๋ํ์ต ๋ฐฉ๋ฒ๊ณผ ํ์ฒ๋ฆฌ ๊ธฐ์ ์ ์ด์ฉํ์ฌ ๊ฐ๋ฅ์ผ ํ์๋ค. ๊ทธ ๋ค ์ฐ๋ฆฌ๋ ๋ชจ์
์ ๋ชจ์ฌํ๋ ํ์ต์ ์ปค๋ฆฌํ๋ผ ํ์ต๊ณผ ๋ณํํ์ฌ ์ฃผ์ด์ง ๋ฆฌํ๊ฒ๋ ์ฐธ์กฐ ๋ชจ์
์ ๋ฐ๋ผ๊ฐ๋ ์ ์ด ์ ์ฑ
์ ์์ฑํ์๋ค. ์ฐ๋ฆฌ๋ "์ ๋ฌธ๊ฐ ์ง๋จ"์ ํ์ตํจ์ผ๋ก ๋ชจ์
๋ฆฌํ๊ฒํ
๋ชจ๋๊ณผ ๋ชจ์
๋ชจ์ฌ ๋ชจ๋์ ์ฑ๋ฅ์ ํฌ๊ฒ ์ฆ๊ฐ์์ผฐ๋ค. ๊ฒฐ๊ณผ์์ ๋ณผ ์ ์๋ฏ, ์ฐ๋ฆฌ์ ์์คํ
์ ์ด์ฉํ์ฌ ์ฌ์ฉ์๊ฐ ์ฌ์กฑ๋ณดํ ๋ก๋ด์ ์์๊ธฐ, ์๊ธฐ, ๊ธฐ์ธ์ด๊ธฐ, ํ ๋ป๊ธฐ, ๊ฑท๊ธฐ, ๋๊ธฐ์ ๊ฐ์ ๋ค์ํ ๋ชจํฐ ๊ณผ์ ๋ค์ ์๋ฎฌ๋ ์ด์
ํ๊ฒฝ๊ณผ ํ์ค์์ ๋ ๋ค ์ํํ ์ ์์๋ค. ์ฐ๋ฆฌ๋ ์ฐ๊ตฌ์ ์ฑ๋ฅ์ ํ๊ฐํ๊ธฐ ์ํ์ฌ ๋ค์ํ ๋ถ์์ ํ์์ผ๋ฉฐ, ํนํ ์ฐ๋ฆฌ ์์คํ
์ ๊ฐ๊ฐ์ ์์๋ค์ ์ค์์ฑ์ ๋ณด์ฌ์ค ์ ์๋ ์คํ๋ค์ ์งํํ์๋ค.A human motion-based interface fuses operator intuitions with the motor capabilities of robots, enabling adaptable robot operations in dangerous environments. However, the challenge of designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is emerged from the different morphology and dynamics of a human controller, leading to an ambiguity of control strategy. We propose a novel control framework that allows human operators to execute various motor skills on a quadrupedal robot by their motion. Our system first retargets the captured human motion into the corresponding robot motion with the operator's intended semantics. The supervised learning and post-processing techniques allow this retargeting skill which is ambiguity-free and suitable for control policy training. To enable a robot to track a given retargeted motion, we then obtain the control policy from reinforcement learning that imitates the given reference motion with designed curriculums. We additionally enhance the system's performance by introducing a set of experts. Finally, we randomize the domain parameters to adapt the physically simulated motor skills to real-world tasks. We demonstrate that a human operator can perform various motor tasks using our system including standing, tilting, manipulating, sitting, walking, and steering on both physically simulated and real quadruped robots. We also analyze the performance of each system component ablation study.1 Introduction 1
2 Related Work 5
2.1 Legged Robot Control 5
2.2 Motion Imitation 6
2.3 Motion-based Control 7
3 Overview 9
4 Motion Retargeting Module 11
4.1 Motion Retargeting Network 12
4.2 Post-processing for Consistency 14
4.3 A Set of Experts for Multi-task Support 15
5 Motion Imitation Module 17
5.1 Background: Reinforcement Learning 18
5.2 Formulation of Motion Imitation 18
5.3 Curriculum Learning over Tasks and Difficulties 21
5.4 Hierarchical Control with States 21
5.5 Domain Randomization 22
6 Results and Analysis 23
6.1 Experimental Setup 23
6.2 Motion Performance 24
6.3 Analysis 28
6.4 Comparison to Other Methods 31
7 Conclusion And Future Work 32
Bibliography 34
Abstract (In Korean) 44
๊ฐ์ฌ์ ๊ธ 45์
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Microsoft Kinect camera and its skeletal tracking capabilities have been
embraced by many researchers and commercial developers in various applications
of real-time human movement analysis. In this paper, we evaluate the accuracy
of the human kinematic motion data in the first and second generation of the
Kinect system, and compare the results with an optical motion capture system.
We collected motion data in 12 exercises for 10 different subjects and from
three different viewpoints. We report on the accuracy of the joint localization
and bone length estimation of Kinect skeletons in comparison to the motion
capture. We also analyze the distribution of the joint localization offsets by
fitting a mixture of Gaussian and uniform distribution models to determine the
outliers in the Kinect motion data. Our analysis shows that overall Kinect 2
has more robust and more accurate tracking of human pose as compared to Kinect
1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics
2015 (ICHI 2015
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