Gonzalo A. García

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The decomposition of surface electromyogram data sets (s-EMG) is studied using blind source separation techniques based on sparseness; namely independent component analysis, sparse nonnegative matrix factorization, and sparse component analysis. When applied to artificial signals we find noticeable differences of algorithm performance depending on the(More)
In spite of the great advances in the mechanical and electronic components of prosthetic hands, they still lack the high number of degrees of freedom present in the real human hand. That is due, not to technical deficiencies, but to the much reduced amount of independent control signals available when using surface electromyography (s-EMG) from the forearm(More)
The purpose of this preliminary work was to evaluate the effectiveness of independent component analysis (ICA) as preprocessing tool for the decomposition of electromyograms (EMG) into their constitutive elements (motor unit action potentials). An experiment was carried out with a healthy subject performing isometric contractions at different force levels.(More)
The cost of the medical treatment of low back pain (LBP) was estimated to be $24 billion in the early 90s. Also, 80% of the LBP is estimated to be due to poor or inappropriate posture. The ultimate goal of the project is to develop a surface electromyography (sEMG)-based device that could be used to prevent and treat LBP by postural re-education or simply(More)
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