Tele-operation System with Reliable Grasping Force Estimation to Compensate for the Time-varying sEMG Feature

Abstract

This paper presents a real-time framework for tele-manipulation by using sEMG signals to estimate both human motion and force intention. Our previous study showed that the ability to detect discrete force levels was not applicable to complex tasks such as grasping, holding, and manipulating various objects with variable force. Consequently, we identified the need to simultaneously track the arm and hand configurations and estimate the grasping force. However, it is difficult to continuously estimate the grasping force because of the time-varying nature of surface Electromyogram (sEMG) signals, even if a force remains constant. To solve such a problem, this study proposes a new regression strategy to enable continuous and proportional measurements and transmission of the grasping force by using sEMG signals in transient and steady-states. A 7-DOF robot arm with a robotic hand was able to remotely imitate a subject via an easily-wearable sEMG and inertia measurement units sensor interface. The experimental results verified that the motion and force capturing system successfully enabled interaction tasks, such as grasping, holding, and releasing motions with objects, with reliable and continuous force estimation.