Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications

Simone Fani, Matteo Bianchi, Sonal Jain, José Simões Pimenta Neto, Scott Boege, Giorgio Grioli, Antonio Bicchi, Marco Santello

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

Original languageEnglish (US)
Article number11
JournalFrontiers in Neurorobotics
Volume10
DOIs
StatePublished - Oct 1 2016

Fingerprint

End effectors
Prosthetics
Electromyography
Kinematics
Controllers
Muscle
Signal to noise ratio
Artificial limbs
Human robot interaction
Mechatronics

Keywords

  • Assistive robotics
  • Kinematics
  • Myoelectric control
  • Prosthetics
  • Rehabilitative robotics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence

Cite this

Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications. / Fani, Simone; Bianchi, Matteo; Jain, Sonal; Neto, José Simões Pimenta; Boege, Scott; Grioli, Giorgio; Bicchi, Antonio; Santello, Marco.

In: Frontiers in Neurorobotics, Vol. 10, 11, 01.10.2016.

Research output: Contribution to journalArticle

Fani, Simone ; Bianchi, Matteo ; Jain, Sonal ; Neto, José Simões Pimenta ; Boege, Scott ; Grioli, Giorgio ; Bicchi, Antonio ; Santello, Marco. / Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications. In: Frontiers in Neurorobotics. 2016 ; Vol. 10.
@article{3d3af8f6796c452ab7859f1d5942da99,
title = "Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications",
abstract = "Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.",
keywords = "Assistive robotics, Kinematics, Myoelectric control, Prosthetics, Rehabilitative robotics",
author = "Simone Fani and Matteo Bianchi and Sonal Jain and Neto, {Jos{\'e} Sim{\~o}es Pimenta} and Scott Boege and Giorgio Grioli and Antonio Bicchi and Marco Santello",
year = "2016",
month = "10",
day = "1",
doi = "10.3389/fnbot.2016.00011",
language = "English (US)",
volume = "10",
journal = "Frontiers in Neurorobotics",
issn = "1662-5218",
publisher = "Frontiers Research Foundation",

}

TY - JOUR

T1 - Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications

AU - Fani, Simone

AU - Bianchi, Matteo

AU - Jain, Sonal

AU - Neto, José Simões Pimenta

AU - Boege, Scott

AU - Grioli, Giorgio

AU - Bicchi, Antonio

AU - Santello, Marco

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

AB - Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

KW - Assistive robotics

KW - Kinematics

KW - Myoelectric control

KW - Prosthetics

KW - Rehabilitative robotics

UR - http://www.scopus.com/inward/record.url?scp=85032647020&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032647020&partnerID=8YFLogxK

U2 - 10.3389/fnbot.2016.00011

DO - 10.3389/fnbot.2016.00011

M3 - Article

VL - 10

JO - Frontiers in Neurorobotics

JF - Frontiers in Neurorobotics

SN - 1662-5218

M1 - 11

ER -