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Although the Hospice Foundation in Constance knew they had a personnel
problem, they were unsure how to begin to fix it. In addition to difficulties in
finding and keeping employees, the Hospice Foundation’s employees were
often on sick leave, adding pressure on remaining staff. Twelve communication
design students in the masters program at the University of Applied
Sciences in Constance (HTWG Konstanz) conducted a study aimed at
identifying the causes for these problems and, more generally, understanding
how the employees work and feel. Even though the methods in this
study are well known, it presents an important prototype for designers and
design researchers because of its success in finding useful insights. It also
serves as a pre-design project briefing for both management and designers.
It demonstrates the usefulness of qualitative methods in providing a deeper
understanding of a complex situation and its usefulness as a strategic tool
and for defining a project’s focus and scope. Ideally, it also provides insights
into health care for the elderly.
Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.