Bipedal locomotion was simulated to generate a pattern of activating muscles for walking using el... more Bipedal locomotion was simulated to generate a pattern of activating muscles for walking using electrical stimulation in persons with spinal cord injury (SCI) or stroke. The simulation presented in this study starts from a model of the body determined with user-specific parameters, individualized with respect to the lengths, masses, inertia, muscle and joint properties. The trajectory used for simulation was recorded from an ablebodied subject while walking with ankle-foot orthoses. A discrete mathematical model and dynamic programming were used to determine the optimal control. A cost function was selected as the sum of the squares of the tracking errors from the desired trajectories, and the weighted sum of the squares of agonist and antagonist activations of the muscle groups acting around the hip and knee joints. The aim of the simulation was to study plausible trajectories keeping in mind the limitations imposed by the spinal cord injury or stroke (e.g., spasticity, decreased range of movements in some joints, limited strength of paralyzed, externally activated muscles). If the muscles were capable of generating the movements required and the trajectory was achieved, then the simulation provided two kinds of information: 1) timing of the onset and offset of muscle activations with respect to the various gait events and 2) patterns of activation with respect to the maximum activation. These results are important for synthesizing a rule-based controller.
This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electri... more This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electrical stimulation for the augmentation of walking. The automatic control relates to the timing of stimulation of four muscles. The sensor system comprises accelerometers and force-sensing resistors. The automatic control implements IF-THEN rules designed by mapping of sensors and muscle activation patterns. The new system was tested in 13 acute stroke patients assigned to a FET group or a control (CON) group. Both groups were treated with a standard rehabilitation program and 45 min of walking daily for 5 days over the course of 4 weeks. The FET group received electrical stimulation during walking. The Fugl-Meyer (FM) test for the lower extremities, Barthel Index (BI), mean walking velocity (v mean ) over a 6-m distance, and Physiological Cost Index (PCI) were assessed at the entry point and at the end of the treatment. Subjects within the FET and CON groups had comparable baseline outcome measures. In the FET group, we determined significant differences in the mean values of all outcomes between the entry and end points of treatment (p < 0.05), contrary to the CON group where we found no significant differences (p > 0.05). We also found significant differences in the changes of FM, BI, v mean and PCI which occurred during the 4 weeks of treatment between the FET and CON groups (p < 0.05). The statistical strength of the clinical study was low (<70%), suggesting the need for a larger, randomized clinical trial. (J. Kojović), [email protected] (M. Djurić-Jovičić), [email protected] (S. Došen), [email protected] (M.B. Popović), [email protected] (D.B. Popović). 1 Tel.: +381 11 3218348. 2 Tel.: +45 9940 9772. 3 Tel.: +45 9940 8758; fax: +45 9815 4008.
Experimental brain research. Experimentelle Hirnforschung. Expérimentation cérébrale, Jan 1, 2008
Rehabilitation with augmented electrical stimulation can enhance functional recovery after stroke... more Rehabilitation with augmented electrical stimulation can enhance functional recovery after stroke, and cortical plasticity may play a role in this process. The purpose of this study was to compare the effects of three training paradigms on cortical excitability in healthy subjects. Cortical excitability was evaluated by analysing the input–output relationship between transcranial magnetic stimulation intensity and motor evoked potentials (MEPs) from the flexor muscles of the fingers. The study was performed with 25 healthy volunteers who underwent 20-min simulated therapy sessions of: (1) functional electrical stimulation (FES) of the finger flexors and extensors, (2) voluntary movement (VOL) with sensory stimulation, and (3) therapeutic FES (TFES) where the electrical stimulation augmented voluntary activation. TFES training produced a significant increase in MEP magnitude throughout the stimulation range, suggesting an increase in cortical excitability. In contrast, neither the FES nor voluntary movement alone had such an effect. These results suggest that the combination of voluntary effort and FES has greater potential to induce plasticity in the motor cortex and that TFES might be a more effective approach in rehabilitation after stroke than FES or repetitive voluntary training alone.
2006 8th Seminar on Neural Network Applications in Electrical Engineering, 2006
... II. INTEGRATION OFTHE QRS COMPLEX ALGORITHM IN THE VIRTUAL ECG MONITOR Off-line processing of... more ... II. INTEGRATION OFTHE QRS COMPLEX ALGORITHM IN THE VIRTUAL ECG MONITOR Off-line processing of the recorded signals was done in the part of the application called ECGViewer, so algorithm described in [1] was implemented inECG Viewer. ...
We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full ... more We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37 ± 1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.
Bipedal locomotion was simulated to generate a pattern of activating muscles for walking using el... more Bipedal locomotion was simulated to generate a pattern of activating muscles for walking using electrical stimulation in persons with spinal cord injury (SCI) or stroke. The simulation presented in this study starts from a model of the body determined with user-specific parameters, individualized with respect to the lengths, masses, inertia, muscle and joint properties. The trajectory used for simulation was recorded from an ablebodied subject while walking with ankle-foot orthoses. A discrete mathematical model and dynamic programming were used to determine the optimal control. A cost function was selected as the sum of the squares of the tracking errors from the desired trajectories, and the weighted sum of the squares of agonist and antagonist activations of the muscle groups acting around the hip and knee joints. The aim of the simulation was to study plausible trajectories keeping in mind the limitations imposed by the spinal cord injury or stroke (e.g., spasticity, decreased range of movements in some joints, limited strength of paralyzed, externally activated muscles). If the muscles were capable of generating the movements required and the trajectory was achieved, then the simulation provided two kinds of information: 1) timing of the onset and offset of muscle activations with respect to the various gait events and 2) patterns of activation with respect to the maximum activation. These results are important for synthesizing a rule-based controller.
This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electri... more This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electrical stimulation for the augmentation of walking. The automatic control relates to the timing of stimulation of four muscles. The sensor system comprises accelerometers and force-sensing resistors. The automatic control implements IF-THEN rules designed by mapping of sensors and muscle activation patterns. The new system was tested in 13 acute stroke patients assigned to a FET group or a control (CON) group. Both groups were treated with a standard rehabilitation program and 45 min of walking daily for 5 days over the course of 4 weeks. The FET group received electrical stimulation during walking. The Fugl-Meyer (FM) test for the lower extremities, Barthel Index (BI), mean walking velocity (v mean ) over a 6-m distance, and Physiological Cost Index (PCI) were assessed at the entry point and at the end of the treatment. Subjects within the FET and CON groups had comparable baseline outcome measures. In the FET group, we determined significant differences in the mean values of all outcomes between the entry and end points of treatment (p < 0.05), contrary to the CON group where we found no significant differences (p > 0.05). We also found significant differences in the changes of FM, BI, v mean and PCI which occurred during the 4 weeks of treatment between the FET and CON groups (p < 0.05). The statistical strength of the clinical study was low (<70%), suggesting the need for a larger, randomized clinical trial. (J. Kojović), [email protected] (M. Djurić-Jovičić), [email protected] (S. Došen), [email protected] (M.B. Popović), [email protected] (D.B. Popović). 1 Tel.: +381 11 3218348. 2 Tel.: +45 9940 9772. 3 Tel.: +45 9940 8758; fax: +45 9815 4008.
Experimental brain research. Experimentelle Hirnforschung. Expérimentation cérébrale, Jan 1, 2008
Rehabilitation with augmented electrical stimulation can enhance functional recovery after stroke... more Rehabilitation with augmented electrical stimulation can enhance functional recovery after stroke, and cortical plasticity may play a role in this process. The purpose of this study was to compare the effects of three training paradigms on cortical excitability in healthy subjects. Cortical excitability was evaluated by analysing the input–output relationship between transcranial magnetic stimulation intensity and motor evoked potentials (MEPs) from the flexor muscles of the fingers. The study was performed with 25 healthy volunteers who underwent 20-min simulated therapy sessions of: (1) functional electrical stimulation (FES) of the finger flexors and extensors, (2) voluntary movement (VOL) with sensory stimulation, and (3) therapeutic FES (TFES) where the electrical stimulation augmented voluntary activation. TFES training produced a significant increase in MEP magnitude throughout the stimulation range, suggesting an increase in cortical excitability. In contrast, neither the FES nor voluntary movement alone had such an effect. These results suggest that the combination of voluntary effort and FES has greater potential to induce plasticity in the motor cortex and that TFES might be a more effective approach in rehabilitation after stroke than FES or repetitive voluntary training alone.
2006 8th Seminar on Neural Network Applications in Electrical Engineering, 2006
... II. INTEGRATION OFTHE QRS COMPLEX ALGORITHM IN THE VIRTUAL ECG MONITOR Off-line processing of... more ... II. INTEGRATION OFTHE QRS COMPLEX ALGORITHM IN THE VIRTUAL ECG MONITOR Off-line processing of the recorded signals was done in the part of the application called ECGViewer, so algorithm described in [1] was implemented inECG Viewer. ...
We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full ... more We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37 ± 1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.
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