Building interactive virtual environments for simulated training in medicine using VRML and Java/JavaScript

Medicine is a difficult thing to learn. Experimenting with real patients should not be the only option; simulation deserves a special attention here. Virtual Reality Modelling Language (VRML) as a tool for building virtual objects and scenes has a good record of educational applications in medicine, especially for static and animated visualisations of body parts and organs. However, to create computer simulations resembling situations in real environments the required level of interactivity and dynamics is difficult to achieve. In the present paper we describe some approaches and techniques which we used to push the limits of the current VRML technology further toward dynamic 3D representation of virtual environments (VEs). Our demonstration is based on the implementation of a virtual baby model, whose vital signs can be controlled from an external Java application. The main contributions of this work are: (a) outline and evaluation of the three-level VRML/Java implementation of the dynamic virtual environment, (b) proposal for a modified VRML Timesensor node, which greatly improves the overall control of system performance, and (c) architecture of the prototype distributed virtual environment for training in neonatal resuscitation comprising the interactive virtual newborn, active bedside monitor for vital signs and full 3D representation of the surgery room.

Correlation-based decomposition of surface electromyograms at low contraction forces

The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MUs was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10 dB, and 93+/-4.6% (mean+/-standard deviation) accuracy with 0 dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.

Optical triple sensor for measuring pH, oxygen and carbon dioxide

A triple sensor unit consisting of opto-chemical sensors for measurement of pH, oxygen and carbon dioxide in bioreactors is presented. The pH and the CO2 sensor are based on the color change of a pH-sensitive dye immobilized on a polymeric support. The resulting changes in absorption are monitored through optical fibers. The oxygen sensor is based on the quenching of the fluorescence of a metal-organic dye. All three sensors are fully LED compatible. The sensitive membranes consist of plastic films and can be stored and replaced conveniently. The sensors are sterilizable with hydrogen peroxide and ethanol. In addition, the pH sensor is steam sterilizable. Accuracy, resolution and reproducibility fulfill the requirements for use in biotechnological applications. Calibration procedures for each sensor are presented. The working principle and the performance of all three sensors are described, with particular emphasis given to their application in bioreactors.