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It is the aim of this thesis to present a visual body weight estimation, which is suitable for medical applications. A typical scenario where the estimation of the body weight is essential, is the emergency treatment of stroke patients: In case of an ischemic stroke, the patient has to receive a body weight adapted drug, to solve a blood clot in a vessel. The accuracy of the estimated weight influences the outcome of the therapy directly. However, the treatment has to start as early as possible after the arrival at a trauma room, to provide sufficient treatment. Weighing a patient takes time, and the patient has to be moved. Furthermore, patients are often not able to communicate a value for their body weight due to their stroke symptoms. Therefore, it is state of the art that physicians guess the body weight. A patient receiving a too low dose has an increased risk that the blood clot does not dissolve and brain tissue is permanently damaged. Today, about one-third gets an insufficient dosage. In contrast to that, an overdose can cause bleedings and further complications. Physicians are aware of this issue, but a reliable alternative is missing.
The thesis presents state-of-the-art principles and devices for the measurement and estimation of body weight in the context of medical applications. While scales are common and available at a hospital, the process of weighing takes too long and can hardly be integrated into the process of stroke treatment. Sensor systems and algorithms are presented in the section for related work and provide an overview of different approaches.
The here presented system -- called Libra3D -- consists of a computer installed in a real trauma room, as well as visual sensors integrated into the ceiling. For the estimation of the body weight, the patient is on a stretcher which is placed in the field of view of the sensors. The three sensors -- two RGB-D and a thermal camera -- are calibrated intrinsically and extrinsically. Also, algorithms for sensor fusion are presented to align the data from all sensors which is the base for a reliable segmentation of the patient.
A combination of state-of-the-art image and point cloud algorithms is used to localize the patient on the stretcher. The challenges in the scenario with the patient on the bed is the dynamic environment, including other people or medical devices in the field of view.
After the successful segmentation, a set of hand-crafted features is extracted from the patient's point cloud. These features rely on geometric and statistical values and provide a robust input to a subsequent machine learning approach. The final estimation is done with a previously trained artificial neural network.
The experiment section offers different configurations of the previously extracted feature vector. Additionally, the here presented approach is compared to state-of-the-art methods; the patient's own assessment, the physician's guess, and an anthropometric estimation. Besides the patient's own estimation, Libra3D outperforms all state-of-the-art estimation methods: 95 percent of all patients are estimated with a relative error of less than 10 percent to ground truth body weight. It takes only a minimal amount of time for the measurement, and the approach can easily be integrated into the treatment of stroke patients, while physicians are not hindered.
Furthermore, the section for experiments demonstrates two additional applications: The extracted features can also be used to estimate the body weight of people standing, or even walking in front of a 3D camera. Also, it is possible to determine or classify the BMI of a subject on a stretcher. A potential application for this approach is the reduction of the radiation dose of patients being exposed to X-rays during a CT examination.
During the time of this thesis, several data sets were recorded. These data sets contain the ground truth body weight, as well as the data from the sensors. They are available for the collaboration in the field of body weight estimation for medical applications.
There is great interest in affordable, precise and reliable metrology underwater:
Archaeologists want to document artifacts in situ with high detail.
In marine research, biologists require the tools to monitor coral growth and geologists need recordings to model sediment transport.
Furthermore, for offshore construction projects, maintenance and inspection millimeter-accurate measurements of defects and offshore structures are essential.
While the process of digitizing individual objects and complete sites on land is well understood and standard methods, such as Structure from Motion or terrestrial laser scanning, are regularly applied, precise underwater surveying with high resolution is still a complex and difficult task.
Applying optical scanning techniques in water is challenging due to reduced visibility caused by turbidity and light absorption.
However, optical underwater scanners provide significant advantages in terms of achievable resolution and accuracy compared to acoustic systems.
This thesis proposes an underwater laser scanning system and the algorithms for creating dense and accurate 3D scans in water.
It is based on laser triangulation and the main optical components are an underwater camera and a cross-line laser projector.
The prototype is configured with a motorized yaw axis for capturing scans from a tripod.
Alternatively, it is mounted to a moving platform for mobile mapping.
The main focus lies on the refractive calibration of the underwater camera and laser projector, the image processing and 3D reconstruction.
For highest accuracy, the refraction at the individual media interfaces must be taken into account.
This is addressed by an optimization-based calibration framework using a physical-geometric camera model derived from an analytical formulation of a ray-tracing projection model.
In addition to scanning underwater structures, this work presents the 3D acquisition of semi-submerged structures and the correction of refraction effects.
As in-situ calibration in water is complex and time-consuming, the challenge of transferring an in-air scanner calibration to water without re-calibration is investigated, as well as self-calibration techniques for structured light.
The system was successfully deployed in various configurations for both static scanning and mobile mapping.
An evaluation of the calibration and 3D reconstruction using reference objects and a comparison of free-form surfaces in clear water demonstrate the high accuracy potential in the range of one millimeter to less than one centimeter, depending on the measurement distance.
Mobile underwater mapping and motion compensation based on visual-inertial odometry is demonstrated using a new optical underwater scanner based on fringe projection.
Continuous registration of individual scans allows the acquisition of 3D models from an underwater vehicle.
RGB images captured in parallel are used to create 3D point clouds of underwater scenes in full color.
3D maps are useful to the operator during the remote control of underwater vehicles and provide the building blocks to enable offshore inspection and surveying tasks.
The advancing automation of the measurement technology will allow non-experts to use it, significantly reduce acquisition time and increase accuracy, making underwater metrology more cost-effective.