From A to Z: Wearable technology explained

Wearable technology (WT) has become a viable means to provide low-cost clinically sensitive data for more informed patient assessment. The benefit of WT seems obvious: small, worn discreetly in any environment, personalised data and possible integration into communication networks, facilitating remote monitoring. Yet, WT remains poorly understood and technology innovation often exceeds pragmatic clinical demand and use. Here, we provide an overview of the common challenges facing WT if it is to transition from novel gadget to an efficient, valid and reliable clinical tool for modern medicine. For simplicity, an A–Z guide is presented, focusing on key terms, aiming to provide a grounded and broad understanding of current WT developments in healthcare

Peripheral photoplethysmography variability analysis of sepsis patients

Sepsis is associated with impairment in autonomic regulatory function. This work investigates the application of heart rate and photoplethysmogram (PPG) waveform variability analysis in differentiating two categories of sepsis, namely systemic inflammatory response syndrome (SIRS) and severe sepsis. Electrocardiogram-derived heart period (RRi) and PPG waveforms, measured from fingertips (Fin-PPG) and earlobes (Ear-PPG), of Emergency Department sepsis patients (n = 28) with different disease severity, were analysed by spectral technique, and were compared to control subjects (n = 10) in supine and 80° head-up tilted positions. Analysis of covariance (ANCOVA) was applied to adjust for the confounding factor of age. Low-frequency (LF, 0.04-0.15 Hz), mid-frequency (MF, 0.09-0.15 Hz) and high-frequency (HF, 0.15-0.60 Hz) powers were computed. The normalised MF power in Ear-PPG (MFnu Ear) was significantly reduced in severe sepsis patients with hyperlactataemia (lactate > 2 mmol/l), compared to SIRS patients (P 0.05), suggesting that there may be a link between 0.1 Hz ear blood flow oscillation and tissue metabolic changes in sepsis, in addition to autonomic factors. The study highlighted the value of PPG spectral analysis in the non-invasive assessment of peripheral vascular regulation in sepsis patients, with potential implications in monitoring the progression of sepsis

Optimisation of Ionic Models to Fit Tissue Action Potentials: Application to 3D Atrial Modelling

A 3D model of atrial electrical activity has been developed with spatially heterogeneous electrophysiological properties. The atrial geometry, reconstructed from the male Visible Human dataset, included gross anatomical features such as the central and peripheral sinoatrial node (SAN), intra-atrial connections, pulmonary veins, inferior and superior vena cava, and the coronary sinus. Membrane potentials of myocytes from spontaneously active or electrically paced in vitro rabbit cardiac tissue preparations were recorded using intracellular glass microelectrodes. Action potentials of central and peripheral SAN, right and left atrial, and pulmonary vein myocytes were each fitted using a generic ionic model having three phenomenological ionic current components: one time-dependent inward, one time-dependent outward, and one leakage current. To bridge the gap between the single-cell ionic models and the gross electrical behaviour of the 3D whole-atrial model, a simplified 2D tissue disc with heterogeneous regions was optimised to arrive at parameters for each cell type under electrotonic load. Parameters were then incorporated into the 3D atrial model, which as a result exhibited a spontaneously active SAN able to rhythmically excite the atria. The tissue-based optimisation of ionic models and the modelling process outlined are generic and applicable to image-based computer reconstruction and simulation of excitable tissue

Time-Frequency Based Features for Classification of Walking Patterns

The analysis of gait data has been a challenging problem and several new approaches have been proposed in recent years. This paper describes a novel front-end for classification of gait patterns using data obtained from a tri-axial accelerometer. The novel features consist of delta features, low and high frequency signal variations and energy variations in both frequency bands. The back-end of the system is a Gaussian mixture model based classifier. Using Bayesian adaptation, an overall classification accuracy of 96.1 % was achieved for five walking patterns in six subjects. Index Terms—Gait patterns, accelerometry, ambulatory monitoring, Gaussian mixture model

Falls Management: Detection and Prevention, Using a Waist Mounted Tri-axial Accelerometer

Abstract-We describe a distributed falls management system capable of real-time falls detection in an unsupervised living context and remote longitudinal tracking of falls risk parameters using a waist-mounted triaxial accelerometer. A self-administrable falls risk assessment is used to facilitate falls prevention. A web-interface allows clinicians to monitor the status of individuals and track their compliance with exercise interventions. Early identification of increased falls risk allows targeted interventions to be promptly administered. Real-time detection of falls allows immediate emergency response protocols to be deployed, reducing morbidity and increasing the independence of the community-dwelling elderly community