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The aim of the HMA group is to extend methodology and theory of detailed analysis of surface EMG signals. There will be developed methods for computer assisted analysis of muscoloskeletal system function and neurophysiological reflexes for control of muscoskeletal system function. There will be acquired data for improvement of diagnosis and therapy of disorders in muscoskeletal system and human movement disorders. Automated processing and evaluation of HD-EMG will be used for these tasks. We aim to search for possibilities of AI method applications, adaptive algorithms and neural networks to automatically extract important information from HD-EMG recording. Further we aim to develop algorithms utilizing HD-EMG feedback and visualization of human muscoloskeletal activity on 3D model during real-time measurement. Extracted knowledge will help to improve diagnosis and therapy of muscoloskeletal disorders and human movement disorders.
Following methods and approaches will be used to reach the above mentioned goals: Advanced methods of biomedical signals filtration and data; new AI methods for signal processing, filtration and feature extraction. Design of new methodologies for application of feature extraction methods to large scale biomedical data. Applicaiton of AI methods for design of suitable classifiers, decision trees for evaluation of high density surface EMG and their combination for finding suitable parts of data to be evaluated by physicians, or discovering artifacts for lowering demands of physicians´ workload. Utilization of AI methods for signal processing and searching regions of interests in large scale data space. Design and realization of PSG device with high density data recording (at minimum 256 bipolar channels that can be used universally for various physiological data measurement).
Responsible: Václav Křemen