Stiffness-Estimation-Based Grasping Force Fuzzy Control for Underactuated Prosthetic Hands

Abstract

Human hands can rapidly perceive the stiffness of an object and quickly adapt to different soft and hard objects by adjusting the grasp force of each finger. However, patients with upper-limb loss have difficulty in outputting accurate grasp force based on objects stiffness information, as the efferent and afferent feedback of nerve channels was losing. To realize stiffness-based force control on a prosthetic hand, it is necessary to know the stiffness of the object to be grasped in advance. This article proposed a novel method to estimate the stiffness of the grasped object based on a kinematics model of prosthetic fingers. Using underactuated mechanisms, the stiffness of the grasped object can be derived based on the contact stiffness information that is obtained upon contact between the prosthetic hand and the target object. Based on the estimation of the stiffness of the grasped object, we developed a control strategy to adjust the inappropriate surface electromyography decoding force of the prosthetic hand, which simulated the motion of a human hand. The selected objects in experiments were representative and common objects used in daily life, with stiffness ranging from soft to hard. The proposed method was proven to be effective and feasible during experimental tests, where such a method can be used to quickly differentiate objects with different stiffness values and thus replicate the controllable stiffness-based force of a human hand with a prosthetic hand.