@book{Weber2023,
  author    = {Weber, Benedikt},
  title     = {Human pose estimation for decubitus prophylaxis},
  number    = {153},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-551-4},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-56719},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-567196},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {73},
  year      = {2023},
  abstract  = {Decubitus is one of the most relevant diseases in nursing and the most expensive to treat. It is caused by sustained pressure on tissue, so it particularly affects bed-bound patients. This work lays a foundation for pressure mattress-based decubitus prophylaxis by implementing a solution to the single-frame 2D Human Pose Estimation problem. For this, methods of Deep Learning are employed. Two approaches are examined, a coarse-to-fine Convolutional Neural Network for direct regression of joint coordinates and a U-Net for the derivation of probability distribution heatmaps. We conclude that training our models on a combined dataset of the publicly available Bodies at Rest and SLP data yields the best results. Furthermore, various preprocessing techniques are investigated, and a hyperparameter optimization is performed to discover an improved model architecture. Another finding indicates that the heatmap-based approach outperforms direct regression. This model achieves a mean per-joint position error of 9.11 cm for the Bodies at Rest data and 7.43 cm for the SLP data. We find that it generalizes well on data from mattresses other than those seen during training but has difficulties detecting the arms correctly. Additionally, we give a brief overview of the medical data annotation tool annoto we developed in the bachelor project and furthermore conclude that the Scrum framework and agile practices enhanced our development workflow.},
  language  = {en}
}
@misc{SalzwedelRabeZahnetal.2017,
  author    = {Salzwedel, Annett and Rabe, Sophie and Zahn, Thomas and Neuwirth, Julia and Eichler, Sarah and Haubold, Kathrin and Wachholz, Anne and Reibis, Rona Katharina and V{\"o}ller, Heinz},
  title     = {User Interest in Digital Health Technologies to Encourage Physical Activity},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401872},
  pages     = {8},
  year      = {2017},
  abstract  = {Background: Although the benefits for health of physical activity (PA) are well documented, the majority of the population is unable to implement present recommendations into daily routine. Mobile health (mHealth) apps could help increase the level of PA. However, this is contingent on the interest of potential users. Objective: The aim of this study was the explorative, nuanced determination of the interest in mHealth apps with respect to PA among students and staff of a university. Methods: We conducted a Web-based survey from June to July 2015 in which students and employees from the University of Potsdam were asked about their activity level, interest in mHealth fitness apps, chronic diseases, and sociodemographic parameters. Results: A total of 1217 students (67.30\%, 819/1217; female; 26.0 years [SD 4.9]) and 485 employees (67.5\%, 327/485; female; 42.7 years [SD 11.7]) participated in the survey. The recommendation for PA (3 times per week) was not met by 70.1\% (340/485) of employees and 52.67\% (641/1217) of students. Within these groups, 53.2\% (341/641 students) and 44.2\% (150/340 employees)—independent of age, sex, body mass index (BMI), and level of education or professional qualification—indicated an interest in mHealth fitness apps. Conclusions: Even in a younger, highly educated population, the majority of respondents reported an insufficient level of PA. About half of them indicated their interest in training support. This suggests that the use of personalized mobile fitness apps may become increasingly significant for a positive change of lifestyle.},
  language  = {en}
}