Surface EMG decomposition using a novel approach for blind source separation

We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine the number of active motor units, the motor unit firing rate and the shape of the average action potential in each motor unit

Importance of debriefing in high-fidelity simulations

Vodena razprava (angl. debriefing) je najpomembnejši del učenja s simulacijami visoke stopnje posnemanja resničnosti, v kateri mentor pozove učeče se, da kritično ocenijo znanje in spretnosti, ki so jih pokazali med izvedbo scenarija. Kljub številnim raziskavam, ki proučujejo izobraževanje s simulacijami, je področje vodene razprave še razmeroma slabo opredeljeno. V prispevku so o sodobni literaturi povzete bistvene značilnosti vodene razprave, njene faze, tehnike in metode. Poudarjena je vloga mentorja, saj je učinkovitost vodene razrave v veliki meri odvisna ravno od njegove usposobljenosti. Podane so smernice, s katerimi mentor lahko oceni lastno uspešnost pri vodenju razprave. Prav tako je izpostavljen pomen pri kontinuiranem izobraževanju v kliničnem okolju, saj vodena razprava omogoča oceno uspešnosti izvedbe klinične obravnave in možnosti postavljanja novih strategij s ciljem doseči večjo usposobljenost zdravstvenega tima. Čeprav je vodena razprava temelj izobraževanja s simulacijami visoke stopnje posnemanja resničnosti, je tudi pomemben način učenja v kliničnem okolju. Mnogi vidiki vodene razprave so še vedno slabo raziskani, zato bo temu segmentu v prihodnosti potrebno nameniti večjo pozornost

Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG

This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at fiveforce levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64+-11% for normal/abnormal, 74+-7% for normal/myopathic, 79+-8% for normal/neuropathic, 49+-20% for myopathic/neuropathic, and 63+-8% for normal/myopathic/neuropathic

Spatialized streaming audio in multi-user virtual training environments

Dans cet article, nous décrivons l'ajout d'une communication audio 3D au Virtual Reality Modelling Language (VRML). Celui-ci permet la communication vocale directe entre participants des environnements virtuels d'entraînement. Afin d'obtenir les meilleurs résultats possibles, nous avons étudié divers modèles acoustiques spatiaux et diverses possibilités actuelles du langage VRML. L'adaptation d'un modèle acoustique 3D aux caractéristiques des environnements-cibles d'entraînement médical, ainsi que l'intégration -indépendante de la plateforme utilisée- d'un système de streaming média au langage VRML constituent notre contribution au développement de ces environnements

A Patient-specific Knee Joint Computer Model Using MRI Data and \u27in vivo\u27 Compressive Load from the Optical Force Measuring System

Modelling of patient knee joint from the MRI data and simulating its kinematics is presented. A flexion of the femur with respect to the tibia from $\\rm{0^o}$ to around $\\rm{40^o}$ is simulated. The finite element knee model is driven by compressive load measured \\u27in vivo\\u27 during MRI process by using specially developed optical force measurement system. Predicted kinematics is evaluated against the high-quality model obtained by registration from experimentally gathered low-quality MRI at fixed flexions. Validation pointed out that the mean square error (MSE) for the Euler rotation angles are bellow $\\rm{1.73^o}$, while the MSE for Euler translation is smaller than 5.93 mm