The techniques are often unreliable because the fixed filters are not adequate to deal with the non-stationary artefact encountered in signals recorded during FES. The simplest techniques that work online use blanking windows, fixed response comb and bandpass/high-pass filters to eliminate artefacts 22, 23, 24, 25. This is likely due to the difficulty involved in the related real-time signal processing. However, only a few techniques have been demonstrated to work online. Several techniques exist in the literature for extraction of vEMG from a muscle under FES 19, 20, 21. The m-wave artefact arises from coordinated evoked response of neurones that innervate the stimulated muscles. The delivered charge from the stimulation dissipates slowly affecting a few signal samples and leads to the post-stimulus voltage decay. The transient stimulation artefact is a sharp, large amplitude direct contamination from the electrical stimulation. Note that these are regarded as artefacts here since they obscure the vEMG. The stimulation related artefacts affect EMG recorded from the proximity of an ongoing electrical stimulation, and are namely (1) transient stimulation 17, (2) post stimulus voltage-decay 18 and (3) m-wave 17.
A solution for this issue is required in order to implement an effective proportional method. The occurrence of stimulation related artefacts in the EMG recorded during FES has made this method difficult to implement.
1, voluntary EMG (vEMG) is continuously extracted from a stimulated muscles and used to modulate the intensity \(Q\) of the ongoing stimulation ( \(Q\propto \left|EMG\right|\)). With the proportional method, shown schematically in Fig. This suggests that explicit necessitation of a user’s engagement during FES may be better implemented using a proportional 16 instead of triggered method of FES application. However functional electrical therapy (FET) 1, 2, FES therapy 14 and brain computer interface controlled-FES (BCI-FES) 15 protocols which encourage a user to continuously attempt movement along with an ongoing electrical stimulation have led to improvement in motor function. This basic implementation of the active engagement which only necessitates a user’s action at the start of the stimulation by triggering FES after detecting muscle activities has not conclusively demonstrated an advantage over the passive method 8, 9, 10, 11, 12. With this method stimulation at a set level is delivered when EMG magnitude meets a set threshold.
Electromyogram provides information on neural activities of a muscle and may be used to detect movement intention 13. In order to engage a user during FES a method of triggering the stimulation following the detection of a set level of electromyogram (EMG) has previously been studied 8, 9, 10, 11, 12. The reason for this could be due to the passive nature of the technique as neurophysiological theories and evidence suggest that active engagement is integral to optimal effect of FES 4, 5, 6, 7. Current literature suggests that this application technique shows inconclusive results in movement rehabilitation 3. Instead a preprogramed pattern is used which allows the user to only switch on/off the FES device. Conventionally FES is applied passively such that a user has no control over an ongoing activation of a muscle. It can be used to activate muscles allowing paralysed people with conditions such as spinal cord injury (SCI) and stroke to perform functional tasks in a manner that may lead to motor recovery 2. The Active FES system may inspire further research in neurorehabilitation and assistive technology.įunctional electrical stimulation (FES) is a technique used for movement rehabilitation 1, 2. Results showed that FES intensity modulated by the Active FES system was proportional to intentional movement. Active FES, the resulting EMG-FES system was validated in a typical use case among fifteen patients with tetraplegia. We demonstrated that unlike the classic comb filter approach, the signal extracted with the present technique was coherent with its noise-free version. Here we show an implementation of an entirely software-based solution that resolves the current problems in real-time using an adaptive filtering technique with an optional comb filter to extract voluntary EMG from muscles under FES. Previous attempts to date either poorly extract the voluntary EMG from the artefacts, require a special hardware or are unsuitable for online application. Electrical artefact contamination of voluntary electromyogram (EMG) during FES application makes the technique difficult to implement. Proportional control of FES using voluntary EMG may be used for this purpose. Neurophysiological theories and past studies suggest that intention driven functional electrical stimulation (FES) could be effective in motor neurorehabilitation.