Single EMG Sensor-Driven Robotic Glove Control for Reliable Augmentation of Power Grasping

Abstract

The practical operation of wearable robots requires intuitive, compact, yet reliable control interfaces. However, current myoelectric interfaces based on surface electromyography (EMG) often fail to achieve these requirements by demanding multiple sensors and exhibiting unreliable performance under limb posture changes. In this study, we show that a myoelectric interface on the musculotendinous junctions (MTJs) of the flexor digitorum superficialis (FDS) enables reliable control of a robotic glove with a single EMG sensor by identifying power grasp intentions. We found that the myoelectric signals from the MTJs of the FDS show significantly increased amplitudes exclusively when a power grasp is performed, regardless of the arm posture. We systematically verified that, in identifying power grasp intentions, the proposed single-sensor myoelectric interface even outperforms a five-sensor myoelectric interface around the proximal forearm. By exploiting the unique biological feature of the MTJs, we devised two myoelectric control methods for a robotic glove-Dual-threshold control and Morse-code control-and further showed their performances in practical operations. Dual-threshold control enables direct co-operation between the user and the robotic glove, and Morse-code control provides various command options for the user.