Online subject-independent modeling of sEMG signals for the motion of a single robot joint

The interaction with robotic devices by means of physiological human signals has become of great interest in the last years because of the capability of catching human intention of movement and translate it in a coherent action performed by a robotic platform. Due to the complexity of EMG signals, several studies have been carried out about models built on a single subject (subject-specific). However, the execution of a certain task presents a common underlying behaviour, even if it is performed by different people. This common behaviour leads to some constraints that could be extracted by looking to different interpretations of the task, obtaining a subject-independent model. The few attempts in literature showed the possibility of creating a multiuser interface able to adapt to novel users (subject-independent). Nevertheless, the majority of the studies focused on classification problems, that are only able to determine the type of movement. We improved the state-of-the-art by introducing an online subject-independent framework able to compute the actual trajectory of the robot motion through a regression technique. The framework is based on a Gaussian Mixture Model (GMM) trained through Surface Electromyography (sEMG) signals coming from human subjects. Wavelet Transform has been used to elaborate the sEMG signals in real time. The goodness of the proposed framework has been tested with two different dataset involving various joints for both upper and lower limbs. The achieved results show that our framework could obtain high performances in both accuracy and computational time by reaching significant correlation (>= 0.8). The whole procedure has been tested on two robots, a simulated hand and a humanoid, by remapping the human motion to the robotic platforms in order to verify the proper execution of the original movement.