An sEMG-Based Human-Robot Interface for Robotic Hands Using Machine Learning and Synergies

Developing natural control strategies represents an intriguing challenge in the design of human–robot interface (HRI) systems. The teleoperation of robotic grasping devices, especially in industrial, rescue, and aerospace applications, is mostly based on nonintuitive approaches, such as remote controllers. On the other hand, recent research efforts target solutions that mimic the human ability to manage multifinger grasps and finely modulate grasp impedance. Since electromyography (EMG) contains information about human motion control, it is possible to leverage such neuromuscular knowledge to teleoperate robotic hands for grasping tasks. In this paper, we present an HRI system based on eight fully differential EMG sensors connected to a wearable sensor node for acquisition and processing. By virtue of a novel bio-inspired approach, the embedded myocontroller merges pattern recognition and factorization techniques to combine a natural selection of the robotic hand configuration with the proportional control of the related grasps. The HRI system has been fully designed, implemented, and tested on two robotic hands: a dexterous anthropomorphic hand and a three-fingered industrial gripper mounted on a robotic manipulator. The results of the test performed on four able-bodied subjects show success rates greater than 90% reached in grasping objects that require different hand shapes and impedance regulations for the task completion. The outcomes also show that the users modulate the bio-inspired degrees of control in a natural manner, proving the pertinence of the proposed system for an effective human-like control of robotic grasping devices in a wearable form factor.