Use of myoelectric signals to command mobile entertainment robot by disabled children: Design and control architecture

Severely disabled children have little chance of environmental and social exploration and discovery. This lack of interaction and independency may lead to an idea that they are unable to do anything by themselves. In an attempt to help children in this situation, educational robotics can offer and aid, once it can provide them a certain degree of independency in the exploration of environment. The system developed in this work allows the child to transmit the commands to a robot through myoelectric and movement sensors. The sensors are placed on the child's body so they can obtain information from the body inclination and muscle contraction, thus allowing commanding, through a wireless communication, the mobile entertainment robot to carry out tasks such as play with objects and draw. In this paper, the details of the robot design and control architecture are presented and discussed. With this system, disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities. © 2012 IEEE.

Regulation of visfatin by microbial and biomechanical signals in PDL cells

Objectives: This in vitro study was established to examine whether visfatin thought to be a link between periodontitis and obesity is produced by periodontal ligament (PDL) cells and, if so, whether its synthesis is modulated by microbial and/or biomechanical signals. Materials and methods: PDL cells seeded on BioFlex® plates were exposed to the oral pathogen Fusobacterium nucleatum ATCC 25586 and/or subjected to biomechanical strain for up to 3 days. Gene expression of visfatin and toll-like receptors (TLR) 2 and 4 was analyzed by RT-PCR, visfatin protein synthesis by ELISA and immunocytochemistry, and NFκB nuclear translocation by immunofluorescence. Results: F. nucleatum upregulated the visfatin expression in a dose- and time-dependent fashion. Preincubation with neutralizing antibodies against TLR2 and TLR4 caused a significant inhibition of the F. nucleatum-upregulated visfatin expression at 1 day. F. nucleatum stimulated the NFκB nuclear translocation. Biomechanical loading reduced the stimulatory effects of F. nucleatum on visfatin expression at 1 and 3 days and also abrogated the F. nucleatum-induced NFκB nuclear translocation at 60 min. Biomechanical loading inhibited significantly the expression of TLR2 and TLR4 at 3 days. The regulatory effects of F. nucleatum and/or biomechanical loading on visfatin expression were also observed at protein level. Conclusions: PDL cells produce visfatin, and this production is enhanced by F. nucleatum. Biomechanical loading seems to be protective against the effects of F. nucleatum on visfatin expression. Clinical relevance: Visfatin produced by periodontal tissues could play a major role in the pathogenesis of periodontitis and the interactions with obesity and other systemic diseases. © 2013 Springer-Verlag Berlin Heidelberg.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.