K.E. Rodriguez Hernandez
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
A Trade-Off Analysis Between Random Noise Attenuation and Muscle State Preservation
A Simulation Study on Stretch Reflex Responses
The surface electromyographic (sEMG) signals that originate from skeletal muscle electrical activity, are used clinically and experimentally to determine muscular behaviour, e.g. amplitude, area under the curve and onset of activity. Surface EMG signals are inevitably contaminated by noise and artefacts from the site between the skin and electrodes, non-target muscles and recording hardware. After recording, signal processing methods like filtering, are used in an attempt to determine the underlying active state of the muscle, portrayed by the motoneuron pool firing. As EMG is in fact a deformed representation of the actual muscle activity, processing is used to extract a more veracious description of the active muscle states.
This study investigated the effects of random noise - which in practice resembles transducer noise -, and filtering on the simulation accuracy of short and long latency muscle stretch responses, extracted from simulated EMG signals. To obtain the deviation from the noiseless signals, a fiber potential model was developed to simulate the EMG surface potentials that used an existing motoneuron pool firing model by Schuurmans et al. 2009. The resulting EMGs were the muscle responses to stretch perturbations at different velocities and amplitudes combinations (1.5, 2, 3, 5 rad/s and 0.06, 0.10, 0.14 rad). Consecutively, the EMG signals were contaminated with different noise intensities (SNR: -1, 2, 5, 7, 9 dB) and then filtered with a \nth{3} order Butterworth low-pass filter, with cut-off frequencies between [1-200Hz]. Finally, the short- and long latency stretch responses areas were calculated and compared between the filtered noiseless and filtered noisy EMG signals, by calculating the difference between the values as a fraction of the value from the noiseless simulated signal. It was found that a signal-to-noise ratio of at least 5 dB with a 85Hz cut-off low-pass filter was necessary to keep the error below 10\% maintaining M1 and M2 characteristics. It was also seen that M1 was more affected than M2 under the same amount of contamination, suggesting different spectral frequency contents between the stretch responses, and different underlying neuronal firing behaviour. The described signal-to-noise ratio thresholds and proposed cut-off frequencies resulting in acceptable signal error, can be used as a reference on accuracy of latency response simulations. The error courses provide information about the way error and signal are attenuated or preserved. Besides, the differences in error course comparing the two latency responses provides an insight into the difference in behaviour between the underlying reflex mechanisms. Apart from the findings the combination of adapted and developed model can be used in future research where noise-free surface potentials are required, and can be further developed to produce veracious EMG signals. ...
This study investigated the effects of random noise - which in practice resembles transducer noise -, and filtering on the simulation accuracy of short and long latency muscle stretch responses, extracted from simulated EMG signals. To obtain the deviation from the noiseless signals, a fiber potential model was developed to simulate the EMG surface potentials that used an existing motoneuron pool firing model by Schuurmans et al. 2009. The resulting EMGs were the muscle responses to stretch perturbations at different velocities and amplitudes combinations (1.5, 2, 3, 5 rad/s and 0.06, 0.10, 0.14 rad). Consecutively, the EMG signals were contaminated with different noise intensities (SNR: -1, 2, 5, 7, 9 dB) and then filtered with a \nth{3} order Butterworth low-pass filter, with cut-off frequencies between [1-200Hz]. Finally, the short- and long latency stretch responses areas were calculated and compared between the filtered noiseless and filtered noisy EMG signals, by calculating the difference between the values as a fraction of the value from the noiseless simulated signal. It was found that a signal-to-noise ratio of at least 5 dB with a 85Hz cut-off low-pass filter was necessary to keep the error below 10\% maintaining M1 and M2 characteristics. It was also seen that M1 was more affected than M2 under the same amount of contamination, suggesting different spectral frequency contents between the stretch responses, and different underlying neuronal firing behaviour. The described signal-to-noise ratio thresholds and proposed cut-off frequencies resulting in acceptable signal error, can be used as a reference on accuracy of latency response simulations. The error courses provide information about the way error and signal are attenuated or preserved. Besides, the differences in error course comparing the two latency responses provides an insight into the difference in behaviour between the underlying reflex mechanisms. Apart from the findings the combination of adapted and developed model can be used in future research where noise-free surface potentials are required, and can be further developed to produce veracious EMG signals. ...
The surface electromyographic (sEMG) signals that originate from skeletal muscle electrical activity, are used clinically and experimentally to determine muscular behaviour, e.g. amplitude, area under the curve and onset of activity. Surface EMG signals are inevitably contaminated by noise and artefacts from the site between the skin and electrodes, non-target muscles and recording hardware. After recording, signal processing methods like filtering, are used in an attempt to determine the underlying active state of the muscle, portrayed by the motoneuron pool firing. As EMG is in fact a deformed representation of the actual muscle activity, processing is used to extract a more veracious description of the active muscle states.
This study investigated the effects of random noise - which in practice resembles transducer noise -, and filtering on the simulation accuracy of short and long latency muscle stretch responses, extracted from simulated EMG signals. To obtain the deviation from the noiseless signals, a fiber potential model was developed to simulate the EMG surface potentials that used an existing motoneuron pool firing model by Schuurmans et al. 2009. The resulting EMGs were the muscle responses to stretch perturbations at different velocities and amplitudes combinations (1.5, 2, 3, 5 rad/s and 0.06, 0.10, 0.14 rad). Consecutively, the EMG signals were contaminated with different noise intensities (SNR: -1, 2, 5, 7, 9 dB) and then filtered with a \nth{3} order Butterworth low-pass filter, with cut-off frequencies between [1-200Hz]. Finally, the short- and long latency stretch responses areas were calculated and compared between the filtered noiseless and filtered noisy EMG signals, by calculating the difference between the values as a fraction of the value from the noiseless simulated signal. It was found that a signal-to-noise ratio of at least 5 dB with a 85Hz cut-off low-pass filter was necessary to keep the error below 10\% maintaining M1 and M2 characteristics. It was also seen that M1 was more affected than M2 under the same amount of contamination, suggesting different spectral frequency contents between the stretch responses, and different underlying neuronal firing behaviour. The described signal-to-noise ratio thresholds and proposed cut-off frequencies resulting in acceptable signal error, can be used as a reference on accuracy of latency response simulations. The error courses provide information about the way error and signal are attenuated or preserved. Besides, the differences in error course comparing the two latency responses provides an insight into the difference in behaviour between the underlying reflex mechanisms. Apart from the findings the combination of adapted and developed model can be used in future research where noise-free surface potentials are required, and can be further developed to produce veracious EMG signals.
This study investigated the effects of random noise - which in practice resembles transducer noise -, and filtering on the simulation accuracy of short and long latency muscle stretch responses, extracted from simulated EMG signals. To obtain the deviation from the noiseless signals, a fiber potential model was developed to simulate the EMG surface potentials that used an existing motoneuron pool firing model by Schuurmans et al. 2009. The resulting EMGs were the muscle responses to stretch perturbations at different velocities and amplitudes combinations (1.5, 2, 3, 5 rad/s and 0.06, 0.10, 0.14 rad). Consecutively, the EMG signals were contaminated with different noise intensities (SNR: -1, 2, 5, 7, 9 dB) and then filtered with a \nth{3} order Butterworth low-pass filter, with cut-off frequencies between [1-200Hz]. Finally, the short- and long latency stretch responses areas were calculated and compared between the filtered noiseless and filtered noisy EMG signals, by calculating the difference between the values as a fraction of the value from the noiseless simulated signal. It was found that a signal-to-noise ratio of at least 5 dB with a 85Hz cut-off low-pass filter was necessary to keep the error below 10\% maintaining M1 and M2 characteristics. It was also seen that M1 was more affected than M2 under the same amount of contamination, suggesting different spectral frequency contents between the stretch responses, and different underlying neuronal firing behaviour. The described signal-to-noise ratio thresholds and proposed cut-off frequencies resulting in acceptable signal error, can be used as a reference on accuracy of latency response simulations. The error courses provide information about the way error and signal are attenuated or preserved. Besides, the differences in error course comparing the two latency responses provides an insight into the difference in behaviour between the underlying reflex mechanisms. Apart from the findings the combination of adapted and developed model can be used in future research where noise-free surface potentials are required, and can be further developed to produce veracious EMG signals.
Ever since human ancestors picked up rocks and used them as tools, technology has enhanced human capabilities. Over the last decades more and more research has been done in trying to enhance strength and endurance by means of an exoskeleton. Reducing the metabolic cost of human walking using lower extremity exoskeletons is one of the challenges that has been taken on by several research groups. Only recently have some of these groups managed to reduce the metabolic cost of walking with the help of an exoskeleton. Understanding how human walking behavior changes and adapts to these new supportive devices is crucial in the quest to better, more efficient exoskeletons. Effects of two control methods, that were proven to be successful in making human walking more energy efficient, on the biomechanics of human subjects were examined by performing lab experiments and using the collected experimental data to run musculoskeletal simulations to compute muscle mechanics and energetics of six lower extremity muscles. Lab experiments, which were completed by eight out of ten recruited subjects, consisted of treadmill walking with and without the exoskeleton, while EMG, respiratory, kinematic and kinetic data was recorded. Processing of this data, the simulation steps taken and the eventual results are presented in this thesis. It was found that walking with the exoskeleton, while it was not giving any assistance, significantly increased the energy consumption rate of subjects, compared to normal walking. This increase was reduced by one of the two support methods, whereas the other had no significant effect on the total metabolic rate.
...
Ever since human ancestors picked up rocks and used them as tools, technology has enhanced human capabilities. Over the last decades more and more research has been done in trying to enhance strength and endurance by means of an exoskeleton. Reducing the metabolic cost of human walking using lower extremity exoskeletons is one of the challenges that has been taken on by several research groups. Only recently have some of these groups managed to reduce the metabolic cost of walking with the help of an exoskeleton. Understanding how human walking behavior changes and adapts to these new supportive devices is crucial in the quest to better, more efficient exoskeletons. Effects of two control methods, that were proven to be successful in making human walking more energy efficient, on the biomechanics of human subjects were examined by performing lab experiments and using the collected experimental data to run musculoskeletal simulations to compute muscle mechanics and energetics of six lower extremity muscles. Lab experiments, which were completed by eight out of ten recruited subjects, consisted of treadmill walking with and without the exoskeleton, while EMG, respiratory, kinematic and kinetic data was recorded. Processing of this data, the simulation steps taken and the eventual results are presented in this thesis. It was found that walking with the exoskeleton, while it was not giving any assistance, significantly increased the energy consumption rate of subjects, compared to normal walking. This increase was reduced by one of the two support methods, whereas the other had no significant effect on the total metabolic rate.