The data acquired during the acquisition phase have been processed in order to extract the useful feature (i.e., ATC parameter) for the classification process. Once the multi-channel sEMG data have been saved on the computer, they have been uploaded to Matlab® to be elaborated. A Matlab script has been implemented to process sEMG signal and extract the ATC feature, which will corresponds to the classifier inputs. The code flow is the following:
• All the muscular sEMG signals of each subject, corresponding to the move-ment execution of the acquisition protocol described in 4.3, have been loaded separately.
• The first 4 seconds of the recording have been removed for the presence of a spike due to the settling of the g.tec system.
Figure 5.1: Settling time g.tec system. Ch2: Masseter.
In these instants of time (shown in Figure 5.1) the system stabilizes so that the acquired signal can reach a certain precision and remain within the specified accuracy range.
• The signals have been pre-filtered hardware by g.HIamp with a high pass filter with a cutoff frequency of 5 Hz and a stopband filter (i.e., Notch filter) at 48-52 Hz. As a result, in order to eliminate low fluctuations signal caused by movement artefacts, and considering a typical sEMG frequency spectrum, the sEMG signals have been further filtered in post-processing using a 10th order Butterworth bandpass filter with a passband between 30-400 Hz. In the Figure 5.2 the module of the frequency response of the implemented filter can be observed.
Figure 5.2: Frequency response Butterworth filter.
Figure 5.3: sEMG signals pre- and post- digital filtering. Muscles activation inves-tigated by each channel: Ch1 - Anterior Temporal, Ch2 - Masseter, Ch3 - Digastric, Ch4 - Zygomaticus major, Ch5 - Corrugator supercilii.
In Figure 5.3 are shown the sEMG signals, acquired during an experimental protocol session from a male subject, before and after software filtering.
• Depending on the movement performed, a labelling procedure has been com-pleted. Thanks to the previous insertion of g.Recorder software markers (shown in Figure 5.4), describing the beginning and the end of a gesture, this often tedious step has been resolved easily. Anyway, sometime the mark-ers have been not inserted with the perferct timing. Therefore, after careful analysis of the signal, they have been appropriately readjusted.
Figure 5.4: g.Recorder screen in Replay mode. On the right are listed the markers’
names and the instants of the start and end of each movement performed.
• In order to extract the ATC feature, a threshold value has been computed for each channel. First of all, the environmental noise characterizing the signal baseline has been evaluated by analyzing 6 s of idle state at the beginning of the recording and, differently to what has been done in Sec. 3.1.4, considering also 10 s in the middle and towards the end of the recording during the closing movements. By monitoring baseline conditions at the beginning and during protocol execution, the possible electrode detachment has been taken into account. During this evaluation, the signal has been also rectified, and the mean and standard deviation of the noise have been computed in order to define the threshold as:
Vth = baseline + mean_noise + 3 ∗ std
Figure 5.5: sEMG signal characterized by a high environmental noise due to a possible displacement electrode. Muscle activation investigated by Ch2 -Masseter.
The threshold value changes according to the channel electrical features and varies among subject because of different skins and bodies conditions. In particular, the baseline noise can be influenced by: electrode placement and state; involuntary facial movements or saliva swallowing; skin condition such as possible beard presence. Figure 5.5 shows masseter muscular activity and highlights some of the problems mentioned above. It can be observed that the signal baseline after first bite movement probably suffered a slight electrode detachment on the masseter, caused by effort caused by biting the nougat.
On the other hand, during the acquisition, the background noise gradually increased because the subject had a beard which reduced the electrode-skin contact.
For these typical issues, the threshold has been evaluated, taking into account the baseline noise for the entire duration of the recording.
In particular, in the case examined, not doing this type of threshold assessment would have led to an inadequate evaluation of the ATC activation related to each movement performed in terms of events.
• An hysteresis of 15 mV has been considered around the computed threshold value in order to take into account spurious muscle signal activation ATC. The algorithm evaluates how many times the sEMG signal has gone above the Vth
± 15 mV threshold. The ATC feature has got by summing up all TC events in a time window of 130 ms.
Figure 5.6: TC events superimposed on the sEMG signal. Channel: Ch1 - Anterior Temporal, Ch2 Masseter, Ch3 Digastric, Ch4 Zygomaticus major, Ch5 -Corrugator supercilii.
• The relative ATC envelopes have been superimposed on the sEMG signals (see Figure 5.7) in order to observe whether the threshold imposed was correct in assessing the muscle activation of each gesture performed.
Figure 5.7: ATC signals envelope of facial protocol. Muscles activation investigated by each channel: Ch1 - Anterior Temporal, Ch2 - Masseter, Ch3 - Digastric, Ch4 - Zygomaticus major, Ch5 - Corrugator supercilii.
• The ATC signal is saved in .csv format, useful for the classification phase.
After analyzing all of the signals, a structure has been created for each subject’s data relating to the three protocol repetitions in order to make them easily accessible for future analysis.