Surface Electromyography (sEMG) signal is the surface electrical signals of the human body, which contains a wealth of information on human action and can be used to determine the user's intent. The purpose of this project is to develop a sEMG-based human-robot interface, which can identify the body's response by signal processing and modelling, and can also transform the response into the motion control instructions, and controls the robot to complete the body movement intentions.
During the random motion of human body, the prediction accuracy of this model is greatly reduced since it is not close to the human actual physiological structure. Also, it cannot be used for the calculation when the muscular force line crosses the joint center. In this project. the elbow joint was selected to implement a new method of muscle modeling, which could solve the problem of accuracy reduction during the random motion of the elbow, while ensuring the real-time processing of the interface.
A new EMG-driven elbow physiological model was developed to predict the elbow flexion and extension. The model was also implemented on a 2-DOF exoskeleton system. A new EMG-driven physiological model for forearm pronation/supination was established. It can predict the forearm continuous rotation movement by the EMG activations from the superficial part of three muscles. The establishment of this forearm physiological model will open up a new way for the prediction of complex joint system with small amplitude motions.
A controller based on the fusion of EMG and force information was proposed. By testing on a 5 DOF upper limb exoskeleton, the effectiveness of EMG based controller (EBC) was experimentally verified. The results showed that the dynamic auxiliary effect of the exoskeleton is obvious, and the physiological model based EBC can adapt to different individuals. This also showed the effectiveness and online adaptability of the EMG-based Neuromuscular Interface proposed by this research.
We also developed biomechanical models which include the patient-specific musculoskeletal properties and model the patients’ effort in muscle level. Two models developed: the patient-specific muscle force estimation model (PMFE) and the patient-specific electromyography (EMG)-driven neuromuscular model (PENm). The PMFE and the PENm predict joint moment and muscle forces through kinematic information and EMG signals, respectively.