Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a rare neurological disease caused by a genetic mutation with a variable presentation and consequent challenging diagnosis, complex follow-up and treatment. At this moment, this condition has no cure and treatment options are under development. One of the disease's implications is a definite and progressive motor impairment that from the early stages compromises walking ability and daily life activities. The detection of this impairment is key for the disease onset diagnosis. With the goal of improving diagnosis of the symptoms and patients' quality of life, the authors have assessed the gait characteristics of subjects suffering from this condition. This contribution shows the results of a preliminary study, using a non-intrusive, markerless vision-based gait analysis tool. To the best of our knowledge, the reported results constitute the first gait analysis data of TTR-FAP mutation carriers. © 2017 IEEE.

Abstract
The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future. © 2008-2011 IEEE.

Abstract
Poly(sodium styrene sulfonate) (pNaSS) was grafted onto poly(epsilon-caprolatone) (PCL) surfaces via ozonation and graft polymerization. The effect of ozonation and polymerization time, as well as the Mohr's salt concentration in the grafting solution, on the degree of grafting was investigated. The degree of grafting was determined through toluidine blue staining. The surface chemical change was characterized by attenuated total reflection Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The result demonstrated that the grafting did not induce any degradation of PCL, and that pNaSS was grafted onto PCL as a thin and covalently stable layer. Furthermore, the modified PCL surface reveals a significant increase in the metabolic activity of fibroblastic cells, as well as a better cell spreading with higher adhesion strength. Consequently, bioactivity of PCL is greatly enhanced by immobilizing a thin layer of pNaSS onto its surface. The grafting of pNaSS is a promising approach to increase the bioactivity of PCL-based materials used in tissue engineering applications, such as ligament reconstruction.

Abstract
Falls of patients are an important issue in hospitals, it causes severe injuries to the patients, increases hospitalization time and treatment costs. The detection of a fall, in time, provides faster rescue to the patient, preventing more serious injuries, as well as saving nursing time. The MovinSense (R) is an electronic device designed for monitoring patients to prevent pressure sores, and the main goal of this work was to develop a new tool for this device, with the purpose of detecting if the patient has fallen from the hospital bed, without changing any of the device original features. Experiments for gathering data samples of inertial signals of falling from the bed were obtained using the device. For fall detection a sensitivity of 72% and specificity of 100% were reached. Another algorithm was developed to detect if the patient got out of his/her bed.