In the Maasstad Hospital Department of Surgery in Rotterdam, two types of patients undergo an abdominal surgery: 1) mobile obese patients 2) patients with cancer. These types of patients need personal contact and guidance, especially patients with cancer. However, nurses are overwhelmed with work and utilize a great amount of their time to measure the vital signs of patients three times a day. These vital signs are: heart rate, blood oxygen level, respiration rate, tympanic temperature and blood pressure. The current method of measuring the vital signs is highly inefficient and vulnerable for human error. Additionally, there is a lack of an alarming system that can notify nurses when patients’ health condition deteriorates. There are existing wearable devices in the market but those are not able to measure all aforementioned vital signs. Additionally, these devices do not fulfil the needs and wishes of the user group. The aim of this study is to design a wearable device that is able to continuously and noninvasively measure vital signs of the patients, considering the level of comfort for patients and level of user-friendliness for the nursing staff. Different research and design methods are applied to gain insight in the context of use, users, stakeholders, human factors, technology and organization. These insights are synthesized in different concepts. From these concepts the most innovative and suitable solution the so-called ‘Omnivisi Earable’ concept is selected. Omnivisi Earable is the product ecosystem proposal of this study. It is a compact and wireless wearable that is able to continuously and non-invasively measure a patient’s vital signs. Due to lack of wires and extra modules, it can be easily attached on the patient’s body. The organic form of the Earable provides high comfort for patients. The time saved to manually measure patients’ vital signs will be used for personal contact and guidance of the patient. Keywords: health data, human factors, continuous monitoring, human centred design, wearable, vital signs, participatory design, medical device, e-health

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.