Grasp-Oriented Myoelectric Interfaces for Robotic Hands: A Minimal-Training Synergy-Based Framework for Intent Detection, Control and Perception

In this paper, a general framework for myoelectric human-robot interfaces for grasp-oriented tasks is presented. In particular, the aspects of intent detection, control and perception are all three integrated in the framework. Particular attention is devoted to provide a minimal-training procedure for the user, and to convey sensory feedback information from contact force sensors in a compact manner, by means of a single vibration motor to foster practical real-world usage of the interface. Specifically, in the proposed framework, the user's motor intention is estimated from electromyographic measurements exploiting the concept of muscular synergy, together with an unsupervised learning approach that enables a short and simple algorithm calibration. Then, the postural synergy concept is exploited in an encoding-decoding fashion, in order to communicate the user's hand closure level to the robotic hand, i.e. exploiting kinematic dimensionality reduction. Moreover, a rough estimation of the internal grasping force applied by the robotic hand is exploited for the generation of a sensory substitution feedback, which is applied to the user by means of vibrotactile stimulation. After describing the general structure of the proposed framework, we show a practical design for a real interface implementation. Finally, experimental tests with 4 naive subjects are reported demonstrating the actual effectiveness of the approach - in terms of mean and standard deviation of grasp strength reference trackings - in fine object grasping tasks.