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The rising use of new media has given rise to public discussions about their possible negative consequences. The social sciences have answered these concerns, providing many studies investigating different media types (e.g., social media, video games) and different related variables (e.g., psychological well-being, academic achievement). Within this big body of research, some research results have confirmed negative associations with frequent media use; other studies have found no or even positive relationships. With heterogeneous results, it is difficult to obtain a clear picture of the relationships and causalities of new media. The method of meta-analysis allows a synthesis of all existing data, providing an overall effect size as well as moderator and mediator analyses which might explain the heterogeneity. Three manuscripts present meta-analytic evidence related to a) the relationship between social media use and academic achievement, b) the relationship between video gaming and overweight, and c) the relationship between social media and psychological correlates. Manuscript #1 found small relationships which depend on the usage pattern of social media. The relationship is positive, as long as social media use is related to school. Manuscript #2 showed that children’s and adolescents’ video gaming is independent from their body mass, while adults who play more have a higher body mass. Manuscript #3 summarized existing meta-analytic evidence that links social media with psychological wellbeing, academic achievement, and narcissism with small to moderate effect sizes. All three manuscripts underscore the potential of meta-analyses to synthesize previous research and to identify moderators. Although meta-analyses are not necessarily superior to other approaches because of their limitations (e.g. limited information or quality of primary studies) they are very promising for media psychology. Meta-analyses can reduce complexity and might be helpful for the communication of research results to the general public.
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.