Corrosion behaviour of porous Ti intended for biomedical applications

Porous Ti implants are being developed inorder to reduce the biomechanical mismatch between theimplant and the bone, as well as increasing the osseointegrationby improving the bone in-growth. Most of the focusin the literature has been on the structural, biological andmechanical characterization of porous Ti whereas there islimited information on the electrochemical characterization.Therefore, the present work aims to study the corrosionbehaviour of porous Ti having 30 and 50 % ofnominal porosity, produced by powder metallurgy routeusing the space holder technique. The percentage, size anddistribution of the pores were determined by image analysis.Electrochemical tests consisting of potentiodynamicpolarization and electrochemical impedance spectroscopywere performed in 9 g/L NaCl solution at body temperature.Electrochemical studies revealed that samples presenteda less stable oxide film at increased porosity, morespecifically, the complex geometry and the interconnectivityof the pores resulted in formation of less protectiveoxide film in the pores.This study was supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalizac¸a˜o (POCI) with the reference project POCI-01-0145- FEDER-006941, Programa de Acc¸o˜es Universita´rias Integradas LusoFrancesas’ (PAUILF TC-12_14), and The Calouste Gulbenkian Foundation through ‘‘Programa de Mobilidade Acade´mica para Professores’’. The authors would also like to acknowledge Prof. Ana Senos (University of Aveiro) and Prof. Jose´ Carlos Teixeira (University of Minho) for the provision of the characterization facilities.info:eu-repo/semantics/publishedVersio

Energy autonomous wearable sensors for smart healthcare: a review

Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for disease prevention, and maintaining optimum, lifelong health quality. This review paper presents recent developments and state-of-the-art research related to three critical elements that enable an EAWS. The first element is wearable sensors, which monitor human body physiological signals and activities. Emphasis is given on explaining different types of transduction mechanisms presented, and emerging materials and fabrication techniques. The second element is the flexible and wearable energy storage device to drive low-power electronics and the software needed for automatic detection of unstable physiological parameters. The third is the flexible and stretchable energy harvesting module to recharge batteries for continuous operation of wearable sensors. We conclude by discussing some of the technical challenges in realizing energy-autonomous wearable sensing technologies and possible solutions for overcoming them

Decoding of Individuated Finger Movements Using Surface EMG and Input Optimization Applying a Genetic Algorithm

In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies