Computerised Decision Support System for Remote Health Monitoring: A Systematic Review

Background

Remote health monitoring plays a major role in handling the critical situation of patients and avoiding death and also enhancing the quality of healthcare services. The effective real time monitoring with accurate decision has to be made in advance with the help of decision making system by continuously acquiring biosignals.

A Simple Method for Measuring the Changeable Mechanical Action of Unloader Knee Braces for Osteoarthritis

Purpose

Today's orthotics should be designed to apply the external orthosis moment to the knee joint solely during the stance phase instead of the entire gait cycle. The aim of this study was to validate the reliability of a simple device for measuring forces at the leg–orthosis interface and describe the behavior of an innovating dynamic unloader knee brace built to interrupt its mechanical action during large knee flexion (swing phase of gait).

Analysis of User Control Attainment in SMR-based Brain Computer Interfaces

Context

Sensorimotor rhythms (SMR) have been the neuronal phenomena of choice in non-invasive EEG-based endogenous brain computer interfaces (BCIs) for more than two decades and SMR-based BCIs have achieved the highest degree of freedom control so far. Nevertheless, they are subject to long periods of training prior to attaining a satisfactory level of control requiring users to learn to modulate their rhythms. The goal of this work is to analyse this problem, discuss the causes of the slow rise in performance and provide recommendations on alternative solutions to quicken control attainment.

Times Varying Spectral Coherence Investigation of Cardiovascular Signals Based on Energy Concentration in Healthy Young and Elderly Subjects by the Adaptive Continuous Morlet Wavelet Transform

Objective

The aims of this study, to investigate the interaction among heart rate variability (HRV), respiratory, systolic arterial blood pressure variability (SABPV) and systolic arterial pressure interval variability (APIV) signals for understanding of cardiovascular control.

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral ($R^2=0.72$) and mirrored bilateral ($R^2=0.72$) reaches and of forearm pronation/supination during unilateral ($R^2=0.77$) and mirrored bilateral ($R^2=0.70$) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral ($R^2=0.82$) and mirrored bilateral ($R^2=0.75$) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension ($R^2=0.72$) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination ($R^2=0.88$) for these subjects, a contradictory result that merits further study.     

Modelling the mechanical response of elastin for arterial tissue

We compare two constitutive models proposed to model the elastinous constituents of an artery. Holzapfel and Weizsäcker [1998. Biomechanical behavior of the arterial wall and its numerical characterization. Comput. Biol. Med. 28, 377–392] attribute a neo-Hookean response, i.e. $\Psi =c(I_1-3))$, to the elastin whilst Zulliger et al. [2004a. A strain energy function for arteries accounting for wall composition and structure. J. Biomech. 37, 989–1000] propose $\Psi =c(I_1-3{)}^{3/2}$. We analyse these constitutive models for two specific cases: (i) uniaxial extension of an elastinous sheet; (ii) inflation of a cylindrical elastinous membrane. For case (i) we illustrate the functional relationships between: (a) the Cauchy stress (CS) and the Green–Lagrange (GL) strain; (b) the tangent modulus (gradient of the CS–GL strain curve) and linearised strain. The predicted mechanical responses are compared with recent uniaxial extension tests on elastin [Gundiah, N., Ratcliffe, M.B., Pruitt, L.A., 2007. Determination of strain energy function for arterial elastin: experiments using histology and mechanical tests. J. Biomech. 40, 586–594; Lillie, M.A., Gosline, J.M., 2007a. Limits to the durability of arterial elastic tissue. Biomaterials 28, 2021–2031; 2007b. Mechanical properties of elastin along the thoracic aorta in the pig. J. Biomech. 40, 2214–2221]. The neo-Hookean model accurately predicts the mechanical response of a single elastin fibre. However, it is unable to accurately capture the mechanical response of arterial elastin, e.g. the initial toe region of arterial elastin (if it exists) or the gradual increase in modulus of arterial elastin that occurs as it is stretched. The alternative constitutive model ($n=\frac{3}{2}$) yields a nonlinear mechanical response that departs from recent uniaxial test data mentioned above, for the same stretch range. For case (ii) we illustrate the pressure–circumferential stretch relationships and the gradients of the pressure–circumferential stretch curves: significant qualitative differences are observed. For the neo-Hookean model, the gradient decreases rapidly to zero, however, for $n=\frac{3}{2}$, the gradient decreases more gradually to a constant value. We conclude that whilst the neo-Hookean model has limitations, it appears to capture more accurately the mechanical response of elastin.