# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 1 Introduction  
Upper limb myoelectric prostheses, also called bionic hands, are electromechanical devices that are attached to the residual limb of amputees in order to replicate the functionality of the human hand and consequently improve the quality of life of these people. Commercial bionic hand models use surface electromyographic (EMG) sensors to capture the electrical activity produced when muscle remnants are activated. However, this is a detection method whose effectiveness is susceptible to external electromagnetic noise, muscle fatigue, or impedance changes in the sensor-skin interface. So, research in the field of myoelectric prostheses is faced with the constant challenge of replicating the functionality of the human hand. This study is part of a broader project, the Open Source Bionic Hand, which aims to develop and control, in real time, a low-cost 3D-printed bionic hand prototype using signals from the muscles of the forearm. In literature it is possible to find previous contributions from this project, focused on the implementation of a prototype of a low-cost controller of a bionic hand, namely from the application of alternative mechanomyographic sensors andnovel and low-cost electrodes, built from a conductive leather material as well as based on desktop 3D printing using conductive PLA (PolyLactic Acid) [1]. The main objective of the work presented in this paper is the implementation and evaluation of the effectiveness of a bimodal EMG/FMG signal acquisition system for the control of a bionic hand. The idea is to counter the limitations of EMG sensors by integrating FMG, which shows benefits such as robustness in the face of impedance changes at the skin interface and sweating and lower sensitivity to sensor positioning. This is despite having its own challenges, such as sensitivity to unintentional movements and external noises. Thus, the simultaneous acquisition of both signals increases the system's immunity to any defect or deviation in operation that may sporadically or systematically condition success in recognizing gestures. The previously mentioned weaknesses of EMG sensors will then be mitigated by the possibility that the classification models also use characteristics of the FMG signal, which will allow learning that leads to a more robust system in the control of the bionic hand. The term FMG, or force myography, describes the various non-invasive techniques that use force sensors to detect voluntary changes associated with the activation/deactivation of superficial muscle groups relative to a default state that usually corresponds to the limb in a relaxed position [2]. It also detects voluntary changes caused by the movement of tendons under the surface of the skin (e.g., in the wrist [3]). The first work on the FMG technique as a modality for the control of myoelectric prostheses was published in 1999 [4], but it was only in the middle of the last decade that it gained traction among researchers, driven by the development of Machine Learning techniques. Several scientific publications [5; 6; 7] present promising results on the possibility of using the FMG technique to predict movement intention in implementations of bionic hand prostheses. More recently, there has been a growing interest in combining sEMG and FMG in order to create more robust control systems to be used by pattern recognition models [8; 9; 10]. What makes the bimodal system interesting is the fact that it detects both electrical and volumetric phenomena associated with muscle contraction. In 2020, [PERSON] et al. [11] proposed a co-localized approach to acquire EMG and FMG simultaneously at the same location, achieving a 10% increase in accuracy in identifying 10 American sign language signals, relative to isolated modalities.