In this paper we present a Human-Robot Interface (HRI) to control a robotic hand via myoelectric signals for grasping tasks. The system is composed by the UB Hand IV as robotic device, and by the Cerebro wearable board as acquisition hardware of the signals from surface skin electrodes. The approach implemented for the HRI relies on a pair of antagonistic flexor-extensor muscles that control both the closure and the grasp stiffness of the robotic hand. Humans accomplish a large variety of grasps thanks to precise impedance regulation: the aim of this study is to emulate this capability on a robotic hand using a user's muscles driven HRI. Experiments conducted with healty subjects showed a short training time together with high success rate of grasp-related tasks, where the users of the HRI were able to naturally modulate the hand's degrees of control by means of forearm muscle contractions. The results show that the system is suitable for further developments for telemanipulation and prosthetic applications.
Experimental evaluation of a sEMG-based human-robot interface for human-like grasping tasks / Meattini, R.; Benatti, S.; Scarcia, U.; Benini, L.; Melchiorri, C.. - (2015), pp. 1030-1035. (Intervento presentato al convegno IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015 tenutosi a Zhuhai Dehan Hotel, No.2 Jida Road, chn nel 2015) [10.1109/ROBIO.2015.7418907].
Experimental evaluation of a sEMG-based human-robot interface for human-like grasping tasks
Benatti S.;
2015
Abstract
In this paper we present a Human-Robot Interface (HRI) to control a robotic hand via myoelectric signals for grasping tasks. The system is composed by the UB Hand IV as robotic device, and by the Cerebro wearable board as acquisition hardware of the signals from surface skin electrodes. The approach implemented for the HRI relies on a pair of antagonistic flexor-extensor muscles that control both the closure and the grasp stiffness of the robotic hand. Humans accomplish a large variety of grasps thanks to precise impedance regulation: the aim of this study is to emulate this capability on a robotic hand using a user's muscles driven HRI. Experiments conducted with healty subjects showed a short training time together with high success rate of grasp-related tasks, where the users of the HRI were able to naturally modulate the hand's degrees of control by means of forearm muscle contractions. The results show that the system is suitable for further developments for telemanipulation and prosthetic applications.
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