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Front Neurol. 2017 Feb 1;8:7. doi: 10.3389/fneur.2017.00007. eCollection 2017.

Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.

Author information

1
School of Biological and Health Systems Engineering , Tempe, AZ , USA.
2
School for Engineering of Matter, Transport, and Energy, Arizona State University , Tempe, AZ , USA.

Abstract

INTRODUCTION:

Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution.

METHODS:

We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach's ability to control hand posture and finger forces.

RESULTS:

Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83-99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution.

DISCUSSION:

This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.

KEYWORDS:

brain–machine interface; machine learning applied to neuroscience; myoelectric hand; neuroprosthesis; neurorobotics

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