In order to improve the conventional stimulator with non-standard parameters and poor efficacy using the passive mode of the treatment, we developed an intelligent stimulator to combine biofeedback with functional electrical stimulation. Through the non-invasive measurement and feature extraction of electromyography signals, a value of root mean square can be obtained based on delta-sigma computational technique. In the newly designed stimulator, electromyography feature extraction and feedback control were used to intelligently control the rehabilitation treatment. Both bidirectional detection technologies for stimulated current and programmable interactive way using touchscreen were employed so that the treatment parameters can be accurately quantified and set up. The experiment showed that the design requirements were achieved.
Algorithms ; Artificial Intelligence ; Electric Stimulation Therapy ; instrumentation ; Electromyography ; methods ; Humans ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation