https://figshare.com/articles/EMG_driven_musculoskeletal_model_for_robot_assisted_stroke_rehabilitation_system/4697485
Neuro-rehabilitation
is a medical process aimed at restoring the sensory and motor functions
of the nervous system via repetitive learning and training process. The
success of neuro-rehabilitation is simply measured by how fast the
patients can recover from the disability. However, the continuous growth
of the number of disabled patients and the lack of resources and
rehabilitation facilities has then questioned the justification of its
success in the future. There are questions about whether the present
approaches of neuro-rehabilitation are still efficient to keep up with
the future growth of the disabled patients which is growing
exponentially. Hence, there is a need for constant advancement in
neuro-rehabilitation engineering to improve the efficiency of automated
rehabilitation. By doing this, the recovery rate of the patients can be
increased, allowing more patients to be rehabilitated even with the lack
of resources and rehabilitation facilities.
The current study explores the utilization of 9 different upper limb’s
muscles to find the best correlation between the electromyography (EMG)
signals of the muscles and the individual torque of the upper joints via
mathematical models. The EMG signals are acquired in real time and
processed by rectification and filtration to eliminate the interference
of the signal. These processed signals are then used as the input
variables in GA; training the optimized correlation coefficients to
minimize the discrepancy between the actual and simulated torque. The
best correlation will be used as a control model in the rehabilitative
robot’s controller as a decision making. Should the patient require
assistance; this model will calculate the amount of assistive force
required and supply it to the patient through the servo to complete the
rehabilitation cycle.
A comparative study, supported by the quantitative data of the
simulation results, indicates that the individual torque of the upper
limb’s joints is best modeled as the inverse logarithm of the EMG
signals of the upper limb muscles with a constant. The estimated torque
calculated from this model is the closest to the actual torque among the
other mathematical models and thus, it has the lowest discrepancy. The
average of 32% discrepancy is calculated throughout 100 generations in
±34 minutes. However, this figure is not attainable upon the integration
of the inverse logarithm mathematical model into the controller mainly
due to the communication rate between the DAQ system and the server.
There is a discrepancy of approximately 10% between the experimental and
simulated results which deems to be quite significant.
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