Learning Articulated Motions From Visual Demonstration

Many functional elements of human homes and workplaces consist of rigid components which are connected through one or more sliding or rotating linkages. Examples include doors and drawers of cabinets and appliances; laptops; and swivel office chairs. A robotic mobile manipulator would benefit from the ability to acquire kinematic models of such objects from observation. This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion. We envision that in future, a machine newly introduced to an environment could be shown by its human user the articulated objects particular to that environment, inferring from these "visual demonstrations" enough information to actuate each object independently of the user. Our method employs sparse (markerless) feature tracking, motion segmentation, component pose estimation, and articulation learning; it does not require prior object models. Using the method, a robot can observe an object being exercised, infer a kinematic model incorporating rigid, prismatic and revolute joints, then use the model to predict the object's motion from a novel vantage point. We evaluate the method's performance, and compare it to that of a previously published technique, for a variety of household objects.Comment: Published in Robotics: Science and Systems X, Berkeley, CA. ISBN: 978-0-9923747-0-

Human upper limb motion analysis for post-stroke impairment assessment using video analytics

Stroke is a worldwide healthcare problem which often causes long-term motor impairment, handicap, and disability. Optical motion analysis systems are commonly used for impairment assessment due to high accuracy. However, the requirement of equipment-heavy and large laboratory space together with operational expertise, makes these systems impractical for local clinic and home use. We propose an alternative, cost-effective and portable, decision support system for optical motion analysis, using a single camera. The system relies on detecting and tracking markers attached to subject's joints, data analytics for calculating relevant rehabilitation parameters, visualization, and robust classification based on graph-based signal processing. Experimental results show that the proposed decision support system has the potential to offer stroke survivors and clinicians an alternative, affordable, accurate and convenient impairment assessment option suitable for home healthcare and tele-rehabilitation

Motion correction of PET/CT images

Indiana University-Purdue University Indianapolis (IUPUI)The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses. In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

Image Registration to Map Endoscopic Video to Computed Tomography for Head and Neck Radiotherapy Patients

The purpose of this work was to explore the feasibility of registering endoscopic video to radiotherapy treatment plans for patients with head and neck cancer without physical tracking of the endoscope during the examination. Endoscopy-CT registration would provide a clinical tool that could be used to enhance the treatment planning process and would allow for new methods to study the incidence of radiation-related toxicity. Endoscopic video frames were registered to CT by optimizing virtual endoscope placement to maximize the similarity between the frame and the virtual image. Virtual endoscopic images were rendered using a polygonal mesh created by segmenting the airways of the head and neck with a density threshold. The optical properties of the virtual endoscope were matched to a calibrated model of the real endoscope. A novel registration algorithm was developed that takes advantage of physical constraints on the endoscope to effectively search the airways of the head and neck for the desired virtual endoscope coordinates. This algorithm was tested on rigid phantoms with embedded point markers and protruding bolus material. In these tests, the median registration accuracy was 3.0 mm for point measurements and 3.5 mm for surface measurements. The algorithm was also tested on four endoscopic examinations of three patients, in which it achieved a median registration accuracy of 9.9 mm. The uncertainties caused by the non-rigid anatomy of the head and neck and differences in patient positioning between endoscopic examinations and CT scans were examined by taking repeated measurements after placing the virtual endoscope in surface meshes created from different CT scans. Non-rigid anatomy introduced errors on the order of 1-3 mm. Patient positioning had a larger impact, introducing errors on the order of 3.5-4.5 mm. Endoscopy-CT registration in the head and neck is possible, but large registration errors were found in patients. The uncertainty analyses suggest a lower limit of 3-5 mm. Further development is required to achieve an accuracy suitable for clinical use