High-Density Electromyography Based Control of Robotic Devices: On the Execution of Dexterous Manipulation Tasks

Electromyography (EMG) based interfaces have been used in various robotics studies ranging from teleoperation and telemanipulation applications to the EMG based control of prosthetic, assistive, or robotic rehabilitation devices. But most of these studies have focused on the decoding of user's motion or on the control of the robotic devices in the execution of simple tasks (e.g., grasping tasks). In this work, we present a learning scheme that employs High Density Electromyography (HD-EMG) sensors to decode a set of dexterous, in-hand manipulation motions (in the object space) based on the myoelectric activations of human forearm and hand muscles. To do that, the subjects were asked to perform roll, pitch, and yaw motions manipulating two different cubes. The first cube was designed to have a center of mass coinciding with the geometric center of the cube, while for the second cube the center of mass was shifted 14 mm to the right (off-centered design). Regarding the acquisition of the myoelectric data, custom HD-EMG electrode arrays were designed and fabricated. Using these arrays, a total of 89 EMG signals were extracted. The object motion decoding was formulated as a regression problem using the Random Forests (RF) technique and the muscle importances were studied using the inherent feature variables importance calculation procedure of the RF. The muscle importance results show that different subjects use different strategies to execute the same motions on same object when the weight is off-centered. Finally, the decoded motions were used to control a five fingered robotic hand in a proof-of-concept application.