Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm.

Abstract

Patients with issues such as cerebral palsy, spinal cord injury, and multiple sclerosis have difficulties with activities of daily living (ADL). Their abilities to perform tasks can be improved through vigorous physical therapy. When that therapy is either not effective or lacking in its progression an assistive robotic device can be used to improve patients' quality of life and help them in accomplishing ADL's. This study presents implementation of an EMG controlled assistive robotic arm to aid patients with upper limb mobility limitations. Using the MYO armband, EMG signals were obtained from three healthy human test subjects and were analyzed in MATLAB® Simulink®. Post signal acquisition, signals were classified to be used as inputs for a Kinova MICO 6 DOF manipulator. Trajectories are planned based on the user EMG signals and robot position data obtained from the Polhemus 6D motion tracker, an IMU-type sensor, which automatically provide position and orientation data. An inverse dynamics controller is developed to drive the robot joints accordingly. Results have shown that the classification accuracy of the EMG signals to control commands for the robot was greater than 90%. The classification accuracy was achieved through the use of a pattern recognition neural network. This preliminary investigation demonstrates the possible future implementation of the system for its intended application.