Protocolos e técnicas de análise de sinais sEMG aplicados à avaliação motora e robótica - Vela, Jhon Freddy Sarmiento (2013)
Technological advances in the last decade opened up the field for the development of information processing systems with high capacity of data storage. These advances in health have evolved in the development of devices for applications in Bioengineering and Biomedical Engineering, supporting the understanding of the physiological behavior, diagnosis, monitoring, treatment and control of various biological changes. Along with technological advances, the amount and complexity of information is increasing, compared to its usefulness and understanding, representing, for different areas of knowledge, a challenge to find viable alternatives for using the attributes of biological systems in the development of new technologies directed to improve the quality of life of human beings. Currently, the development of noninvasive protocols for capturing bioelectric signals are becoming a viable option for the diagnosis of myopathies, motor rehabilitation, biomechanical analysis, development of Human-Machine Interface, and autonomous control of robotic devices for people with severe motor disabilities among other applications. In all cases, the support of computational techniques, such as digital signal processing (DSP), and new algorithms based on artificial intelligence, has opened the opportunity to develop classification techniques for recognizing patterns which can be applied in biotechnology for health. This doctoral thesis develops protocols and techniques for analysis of sEMG signals, consisting of "instructed delay tasks", applied to the motor assessment and rehabilitation estrategies, involving analysis of inclusion-exclusion criteria for clinical history, control variables in experimental environment, capture, acquisition and processing of sEMG signal, digital group, filtering, segmentation, feature selection, classification and pattern recognition. Biotechnological applications with sEMG signals present a quantitative experimental approach in the form of case studies. The first case study is centered on three acquisition protocols for evaluation of proprioceptive knee, control of a robotic wheelchair for people with severe motor disabilities, and manipulation of a mobile robot for children with cognitive and motor disability, using a hybrid sensor (inclination + sEMG), which is a patent derivate of this thesis. The second case study, develops a protocol for acquisition of sEMG signals in order, to support the diagnosis of fibromyalgia using algorithms for evaluation of muscle fatigue in time domain (ARV, RMS) and frequency domain (MNF, MDF, AIF), with 30%, 60% and 80% of MVC. The third case study, develops a protocol for the acquisition of sEMG signals with low density and low level of muscle contraction, with control of the rest, for the recognition of different hand gestures in healthy and amputees, evaluating 14 characteristics , 8 in time domain, and 5 in frequency domain and Fractal Dimension (FD), with several of their combinations, which were classified with computational techniques of artificial intelligence, such as fuzzy logic (FL) and artificial neural networks of MLP type. The results for the first case study, has demonstrated the usefulness of threshold predetermination as RMS and slope, acquired with the hybrid sensor (inclination + sEMG), improving the accuracity sense of positioning in proprioceptive analysis of the knee compared to a commercial electrogoniometer in combination with sEMG signal. The hybrid sensor also was applied to the control of a robotic wheelchair, using head movements for self-displacement of persons with tetraplegia, as well as autonomous manipulation of a mobile robot by people with cognitive and motor disabilities, which was obtained with training, whose performance in interacting with the robot was evaluated by GAS index. In the second case study, the results obtained for assessment of fatigue in people with fibromyalgia (FM)have indicated a relationship between increasing load and muscle pain, especially with 80% of MVC. The linear regression of algorithms RMS, ARV and MNF havshown in both the inclination (α ) and intercept (β) an expected trend in the control group, with positive linear relationship to characteristics in the time domain and negative characteristics to the frequency domain, with 60% MVC, and 60% of isometric segment of sEMG signal, which were obtained with 20 isotonic contractions during flexion-extension of biceps braquii (RMS α = 1.1319, β = 275 706; MNF α = -0470, β = 91 482). In the case of volunteers with FM, the N3 voluntary presented a behavior with the highest expected trend of muscular fatigue at 80% MVC and 60% of isometric segment, obtained during isotonic movement of biceps braquii (RMS α = 5.92 β = 113.33; MNF α = β = -1.21 96.96). Finally, the third case study, identified, with the MLP classifier, a success rate of 94.9% for six movements of individual fingers, including rest (category A), and 97.5% of success rate for seven movements, including: fingers, wrist and grip (category B), both cases, with a combination of features RMS, WL, MAV and ZC. On the other hand, the results obtained by amputee volunteers showed better results with features in time domain, compared to fractal dimension (DF), with success rates of 93.9% using combination RMS, WL and MAV characteristics for category A, and 95.4% of success rate with combination of RMS, WL, MAV and ZC in category B.