During the project we obtained a model for prediction of skeletal muscle composition. Using this model we estimated from our previous data, obtained in two ARRS projects between 2001-2007, skeletal muscle composition in children aged 7-14 years. Today, we are waitinig for manuscript revision.
B.04 Guest lecture
COBISS.SI-ID: 1536944324The software provides fully automatic decomposition of surface electromyograms, recorded in isometric muscle contractions, into contributions of individual motor units, thus, noninvasive investigation of muscle control strategies, also in children and needle-intolerant patients. In the last four years, it has been tested in more than 1000 healthy subjects and patients with neurodegenerative diseases. It is currently being used in investigations of children with cerebral palsy, in computer aided diagnosis of Parkinsonian and essential tremor, in studies of diabetes, in planning of cleft-lip surgeries, in design and evaluation of rehabilitation techniques after stroke and in studies of muscle atrophy and hypertrophy. The areas of other potential applications include ergonomics, training of athletes, studies of impact of microgravity on human motor system and myocontrol of prosthetic devices. The tool became commercially available at the beginning of 2014 (http://demuse.uni-mb.si/)
F.06 Development of a new product
COBISS.SI-ID: 18426390The spinal circuitries combine the information flow from the supraspinal centers with the afferent input to generate the neural codes that drive the human skeletal muscles. The muscles transform the neural drive they receive from alpha motor neurons into motor unit action potentials (electrical activity) and force. Thus, the output of the spinal cord circuitries can be examined noninvasively by measuring the electrical activity of skeletal muscles at the surface of the skin, i.e., the surface electromyogram (EMG). The recorded multi-muscle EMG activity pattern is generated by mixing processes of neural sources that need to be identified from the recorded signals themselves, with minimal or no a-priori information available. Recently, multichannel source separation techniques that rely minimally on a priori knowledge of the mixing process have been developed and successfully applied to surface EMG. They act at different scales of information extraction to identify: i) the activation signals shared by synergistic skeletal muscles, ii) the specific neural activation of individual muscles, separating it from that of nearby muscles, i.e., from crosstalk, and iii) the spike trains of the active motor neurons. This invited topical review discusses the assumptions made by these methods, the challenges and limitations, as well as examples of their current applications.
F.30 Professional assessment of the situation
COBISS.SI-ID: 18016278