# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 2 Materials and Methods
### EMG and FMG Signals  
The EMG signal is a widely used tool in the detection of motion intent in commercial bionic prosthetic applications. However, the search for additional information on muscle activity has motivated the exploration of complementary techniques, such as force myography (FMG). The EMG signal is the electrical expression of muscle activity, in this case captured by surface electrodes placed on the skin on the study muscle. The amplitude of the EMG signal, which is stochastic (random) in nature, is influenced by the strength of muscle contraction and usually ranges from 0 to 10 mV peak-to-peak, or from 0 to 1.5 mV RMS. The EMG signal is particularly useful in the 0-500 Hz frequency range, with the dominant energy in the 50-150 Hz range. This signal characteristic is illustrated in Figure 1, which shows power density spectra of EMG signals from different hand gestures. Figure 1: Power density spectra (in dB/Hz) of EMG signals in hand gestures (from [18]). FMG is a non-invasive technique that makes use of pressure sensors placed on the skin above the muscles to capture changes in pressure and volume associated with the activation and deactivation of superficial muscle groups. Instead of measuring muscle electrical activity like EMG, FMG records mechanical changes, thus capturing distinct information, which can be valuable in the context of bionic prostheses. Although FMG has benefits such as robustness to changes in skin impedance and sweating and less sensitivity to sensor positioning, it faces challenges such as sensitivity to unintentional movements and external interference. For the acquisition of physiological signals, we used BITalino (r)evolution (PLUX Biosignals, Lisbon, Portugal). This platform is distinguished by its ability to integrate a wide diversity of sensors as electromyography (EMG), electrocardiography (ECG), accelerometer (ACC), and many others. In the context of this work, the BITalino board was used to collect EMG and FMG signals. The EMG signals were obtained using two BITalino's own EMG sensors. On the other hand, the capture of FMG signals required the use of two external FSR 402 sensors (Interlink Electronics, Camarillo, CA, USA), which, after a signal conditioning circuit, were integrated into BITalino. Table 1 summarizes the main technical specifications of BITalino (r)evolution. The transmission of data to the computer is done in real time via Bluetooth. In turn, the OpenSignals v2.2.5 software (Plux Wireless Biosignals S.A., Lisbon, Portugal), compatible with the BITalino platform, allows real-time visualization of data from multiple channels and devices (up to 24 channels and 3 devices). In addition, the software allows the export of data in.txt and.h5 formats, as well as their offline viewing. Figure 2 shows an example of visualization of offline signals in Opensignals. \begin{table}
\begin{tabular}{l c} \hline \hline Sampling Rate & 1, 10, 100 or 1000 Hz \\ Analog Inputs & 4 in (A1–A4, 10-bit) + 2 in (A5–A6, 6-bit) + 1 out (8-bit) \\ Digital Inputs & 2 in (1-bit) + 2 out (1-bit) \\ Connectivity & Bluetooth Class II v2.0 (range till 10 m) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BITalino (r)evolution: technical specifications. Figure 2: Previously stored data file in Opensignals, showing EMG (**top**) and FMG (**bottom**) signals for the “hand opening” gesture (channels II to I4 not used). As previously mentioned, the monitoring of the electrical activity of the flexor and extensor muscle groups of the forearm was done using two BITalino EMG sensors, specially designed for sEMG acquisitions. It is compatible with gel and dry electrodes and offers high-quality data with low noise due to its bipolar configuration. The EMG sensor is responsible for analog filtering, amplification, and A/D conversion of the signal. Table 2 presents the technical specifications of BITalino's EMG module sensor. Within the scope of this project, sensors of the FSR 402 model were selected.