Electrotactile Feedback Improves Grip Force Control and Enables Object Stiffness Recognition While Using a Myoelectric Hand

Abstract

Current myoelectric hands are limited in their ability to provide effective sensory feedback to the users, which highly affects their functionality and utility. Although the sensory information of a myoelectric hand can be acquired with equipped sensors, transforming the sensory signals into effective tactile sensations on users for functional tasks is a largely unsolved challenge. The purpose of this study aims to demonstrate that electrotactile feedback of the grip force improves the sensorimotor control of a myoelectric hand and enables object stiffness recognition. The grip force of a sensorized myoelectric hand was delivered to its users via electrotactile stimulation based on four kinds of typical encoding strategies, including graded (G), linear amplitude (LA), linear frequency (LF), and biomimetic (B) modulation. Object stiffness was encoded with the change of electrotactile sensations triggered by final grip force, as the prosthesis grasped the objects. Ten able-bodied subjects and two transradial amputees were recruited to participate in a dual-task virtual eggs test (VET) and an object stiffness discrimination test (OSDT) to quantify the prosthesis users' ability to handle fragile objects and recognize object stiffnesses, respectively. The quantified results showed that with electrotactile feedback enabled, the four kinds of encoding strategies allowed subjects to better able to handle fragile objects with similar performance, and the subjects were able to differentiate four levels of object stiffness at favorable accuracies (>86%) and high manual efficiency. Strategy LA presented the best stiffness discrimination performance, while strategy B was able to reduce the discrimination time but the discrimination accuracy was not better than the other three strategies. Electrotactile feedback also enhanced prosthesis embodiment and improved the users' confidence in prosthetic control. Outcomes indicate electrotactile feedback can be effectively exploited by the prosthesis users for grip force control and object stiffness recognition, proving the feasibility of functional sensory restoration of myoelectric prostheses equipped with electrotactile feedback.

Soft Robotics Enables Neuroprosthetic Hand Design

“…Electrotactile feedback is a plausible noninvasive method to evoke sensations through transcutaneous electrical nerve stimulation on the skin. , Electrotactile feedback is also one of functional electrical stimulation methods, which has been applied over the past 40 years to both upper-limb rehabilitation or motor tasks . The two main units for electrotactile stimulation are the surface electrodes and electric stimulators, which is similar to the invasive feedback .…”

“…Different from the invasive feedback, the noninvasive feedback method can reduce the complex surgical procedures. Thus, the noninvasive feedback can only provide sensory substitution to the body through an artificial sensory channel with a different modality (e.g., mechanotactile, vibrotactile, , and electrotactile − ).…”

Substitutive proprioception feedback of a prosthetic wrist by electrotactile stimulation

“…Homology and somatotopy are the priority factors affecting the acceptability of prosthetic sensory feedback methods because they affect the training periods that patients require ( Raspopovic et al, 2021 ) and acceptance of the feedback device ( Makin et al, 2017 ; Lan et al, 2019 ). In the literature, there are a variety of feedback methods, including invasive electrical stimulation ( Schiefer et al, 2016 ; Vu et al, 2022 ), skin stretching ( Battaglia et al, 2019 ), vibration ( Vargas et al, 2021b ), mechanical pressure ( Godfrey et al, 2016 ), audio ( Gonzalez et al, 2012 ), and electrotactile stimulation ( Franceschi et al, 2017 ; Chai et al, 2022 ). Although the sensations induced by electrotactile stimulation are not somatotopic, users can learn to interpret the feedback with a few days of training ( Bensmaia et al, 2020 ).…”

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

“…The introduction of direct feedback modalities can prevent amputees to rely exclusively on sight (Biddiss et al, 2007 ; Pylatiuk et al, 2007 ), reducing the mental effort and, therefore, facilitating the communication between user intention and prosthesis action (Markovic et al, 2018 ; Valle et al, 2018 ; Clemente et al, 2019 ). In fact, it has been demonstrated that the introduction of haptic feedback improves the control of the prosthesis (Mayer et al, 2020 ; Sensinger and Dosen, 2020 ; Yildiz et al, 2020 ; Chai et al, 2022 ) due to its fundamental role during human–objects interactions (Hsiao et al, 2011 ; Valle et al, 2018 ; Pena et al, 2019 ; Di Pino et al, 2020 ; Shehata et al, 2020 ; Raspopovic et al, 2021 ), allowing subjects to embody the device (Antfolk et al, 2013 ; Svensson et al, 2017 ; Raspopovic et al, 2021 ), hence, improving the compliance among the user, the prosthesis, and the grasped objects (Osborn et al, 2016 ). In the literature, this interaction is mainly assessed by providing grasp force or proprioceptive information (Stephens-Fripp et al, 2018 ).…”