TY - JOUR
T1 - Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis
AU - Piazza, Cristina
AU - Rossi, Matteo
AU - Catalano, Manuel G.
AU - Bicchi, Antonio
AU - Hargrove, Levi J.
N1 - Funding Information:
Manuscript received December 13, 2019; revised May 20, 2020 and July 27, 2020; accepted August 10, 2020. Date of publication August 17, 2020; date of current version October 8, 2020. This work was supported in part by the European Union through the Horizon 2020 Research and Innovation Program under Grant 688857 (SoftPro), in part by the ERC Programme under Grant 810346 (Natural Bionics), and in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institute of Health (NIH) under Award 5R01HD094861-02. (Corresponding author: Cristina Piazza.) Cristina Piazza is with the Regenstein Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL 60611 USA, and also with the Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611 USA (e-mail: cristina.piazza@ing.unipi.it).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - While natural movements result from fluid coordination of multiple joints, commercial upper-limb prostheses are still limited to sequential control of multiple degrees of freedom (DoFs), or constrained to move along predefined patterns. To control multiple DoFs simultaneously, a probability-weighted regression (PWR) method has been proposed and has previously shown good performance with intramuscular electromyographic (EMG) sensors. This study aims to evaluate the PWR method for the simultaneous and proportional control of multiple DoFs using surface EMG sensors and compare the performance with a classical direct control strategy. To extract the maximum number of DoFs manageable by a user, a first analysis was conducted in a virtually simulated environment with eight able-bodied and four amputee subjects. Results show that, while using surface EMG degraded the PWR performance for the 3-DoFs control, the algorithm demonstrated excellent achievements in the 2-DoFs case. Finally, the two methods were compared on a physical experiment with amputee subjects using a hand-wrist prosthesis composed of the SoftHand Pro and the RIC Wrist Flexor. Results show comparable outcomes between the two controllers but a significantly higher wrist activation time for the PWR method, suggesting this novel method as a viable direction towards a more natural control of multi-DoFs.
AB - While natural movements result from fluid coordination of multiple joints, commercial upper-limb prostheses are still limited to sequential control of multiple degrees of freedom (DoFs), or constrained to move along predefined patterns. To control multiple DoFs simultaneously, a probability-weighted regression (PWR) method has been proposed and has previously shown good performance with intramuscular electromyographic (EMG) sensors. This study aims to evaluate the PWR method for the simultaneous and proportional control of multiple DoFs using surface EMG sensors and compare the performance with a classical direct control strategy. To extract the maximum number of DoFs manageable by a user, a first analysis was conducted in a virtually simulated environment with eight able-bodied and four amputee subjects. Results show that, while using surface EMG degraded the PWR performance for the 3-DoFs control, the algorithm demonstrated excellent achievements in the 2-DoFs case. Finally, the two methods were compared on a physical experiment with amputee subjects using a hand-wrist prosthesis composed of the SoftHand Pro and the RIC Wrist Flexor. Results show comparable outcomes between the two controllers but a significantly higher wrist activation time for the PWR method, suggesting this novel method as a viable direction towards a more natural control of multi-DoFs.
KW - Upper limb prosthesis
KW - simultaneous control
KW - soft robotics
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U2 - 10.1109/TNSRE.2020.3016909
DO - 10.1109/TNSRE.2020.3016909
M3 - Article
C2 - 32804650
AN - SCOPUS:85092463082
VL - 28
SP - 2286
EP - 2295
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
SN - 1534-4320
IS - 10
M1 - 9169706
ER -