scholarly journals Game-Based Rehabilitation for Myoelectric Prosthesis Control

2017 ◽  
Vol 5 (1) ◽  
pp. e3 ◽  
Author(s):  
Cosima Prahm ◽  
Ivan Vujaklija ◽  
Fares Kayali ◽  
Peter Purgathofer ◽  
Oskar C Aszmann
Keyword(s):  
Myoelectric Prosthesis ◽  
Prosthesis Control

Nerve Transfers in Transhumeral Amputation: Creating Myoneurosomes for Improved Myoelectric Prosthesis Control

2005 ◽  
Vol 21 (07) ◽  
Author(s):  
Hakim Said ◽  
Todd Kuiken ◽  
Robert Lipzchutz ◽  
Laura Miller ◽  
Gregory Dumanian
Keyword(s):  
Myoelectric Prosthesis ◽  
Nerve Transfers ◽  
Prosthesis Control

scholarly journals Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric J. Earley ◽  
Reva E. Johnson ◽  
Jonathon W. Sensinger ◽  
Levi J. Hargrove
Keyword(s):  
Sensory Feedback ◽  
Feedforward Control ◽  
Feedback System ◽  
Augmented Feedback ◽  
Myoelectric Prosthesis ◽  
Reaching Task ◽  
Prosthesis Control ◽  
High Uncertainty ◽  
Relevant Factors ◽  
Positional Control

AbstractAccurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision; some studies showed benefits, while others did not. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty (e.g. joint speed). In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found that overall reaching errors were reduced after perturbed control, but did not significantly improve steady-state reaches. Furthermore, we found that feedback about the joint speed of the myoelectric prosthesis control improved the adaptation rate of biological limb movements, which may have resulted from high prosthesis control noise and strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

Real time ECG artifact removal for myoelectric prosthesis control

2007 ◽  
Vol 28 (4) ◽  
pp. 397-413 ◽  
Author(s):  
Ping Zhou ◽  
Blair Lock ◽  
Todd A Kuiken
Keyword(s):  
Real Time ◽  
Artifact Removal ◽  
Myoelectric Prosthesis ◽  
Prosthesis Control

scholarly journals The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee

2004 ◽  
Vol 28 (3) ◽  
pp. 245-253 ◽  
Author(s):  
T. A. Kuiken ◽  
G. A. Dumanian ◽  
R. D. Lipschutz ◽  
L. A. Miller ◽  
K. A. Stubblefield
Keyword(s):  
Degrees Of Freedom ◽  
Subcutaneous Fat ◽  
Pectoralis Major ◽  
Myoelectric Prosthesis ◽  
Targeted Muscle Reinnervation ◽  
Upper Limb Prosthesis ◽  
Muscle Reinnervation ◽  
Major Region ◽  
Prosthesis Control ◽  
Nerve Muscle

A novel method for the control of a myoelectric upper limb prosthesis was achieved in a patient with bilateral amputations at the shoulder disarticulation level. Four independently controlled nerve-muscle units were created by surgically anastomosing residual brachial plexus nerves to dissected and divided aspects of the pectoralis major and minor muscles. The musculocutaneous nerve was anastomosed to the upper pectoralis major; the median nerve was transferred to the middle pectoralis major region; the radial nerve was anastomosed to the lower pectoralis major region; and the ulnar nerve was transferred to the pectoralis minor muscle which was moved out to the lateral chest wall. After five months, three nerve-muscle units were successful (the musculocutaneous, median and radial nerves) in that a contraction could be seen, felt and a surface electromyogram (EMG) could be recorded. Sensory reinnervation also occurred on the chest in an area where the subcutaneous fat was removed. The patient was fitted with a new myoelectric prosthesis using the targeted muscle reinnervation. The patient could simultaneously control two degrees-of-freedom with the experimental prosthesis, the elbow and either the terminal device or wrist. Objective testing showed a doubling of blocks moved with a box and blocks test and a 26% increase in speed with a clothes pin moving test. Subjectively the patient clearly preferred the new prosthesis. He reported that it was easier and faster to use, and felt more natural.

scholarly journals Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control

2022 ◽  
pp. 1-1
Author(s):  
Heather Elizabeth Williams ◽  
Ahmed W. Shehata ◽  
Michael Rory Dawson ◽  
Erik Scheme ◽  
Jacqueline Susanne Hebert ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Myoelectric Prosthesis ◽  
Prosthesis Control

scholarly journals Neural interfacing architecture enables enhanced motor control and residual limb functionality postamputation

2021 ◽  
Vol 118 (9) ◽  
pp. e2019555118
Author(s):  
Shriya S. Srinivasan ◽  
Samantha Gutierrez-Arango ◽  
Ashley Chia-En Teng ◽  
Erica Israel ◽  
Hyungeun Song ◽  
...  
Keyword(s):  
Phantom Limb ◽  
Residual Limb ◽  
Myoelectric Prosthesis ◽  
Knee Amputation ◽  
Ankle Joints ◽  
Prosthesis Control ◽  
Muscle Excursion ◽  
Proprioceptive Inputs ◽  
Neuromuscular Dynamics

Despite advancements in prosthetic technologies, patients with amputation today suffer great diminution in mobility and quality of life. We have developed a modified below-knee amputation (BKA) procedure that incorporates agonist–antagonist myoneural interfaces (AMIs), which surgically preserve and couple agonist–antagonist muscle pairs for the subtalar and ankle joints. AMIs are designed to restore physiological neuromuscular dynamics, enable bidirectional neural signaling, and offer greater neuroprosthetic controllability compared to traditional amputation techniques. In this prospective, nonrandomized, unmasked study design, 15 subjects with AMI below-knee amputation (AB) were matched with 7 subjects who underwent a traditional below-knee amputation (TB). AB subjects demonstrated significantly greater control of their residual limb musculature, production of more differentiable efferent control signals, and greater precision of movement compared to TB subjects (P < 0.008). This may be due to the presence of greater proprioceptive inputs facilitated by the significantly higher fascicle strains resulting from coordinated muscle excursion in AB subjects (P < 0.05). AB subjects reported significantly greater phantom range of motion postamputation (AB: 12.47 ± 2.41, TB: 10.14 ± 1.45 degrees) when compared to TB subjects (P < 0.05). Furthermore, AB subjects also reported less pain (12.25 ± 5.37) than TB subjects (17.29 ± 10.22) and a significant reduction when compared to their preoperative baseline (P < 0.05). Compared with traditional amputation, the construction of AMIs during amputation confers the benefits of enhanced physiological neuromuscular dynamics, proprioception, and phantom limb perception. Subjects’ activation of the AMIs produces more differentiable electromyography (EMG) for myoelectric prosthesis control and demonstrates more positive clinical outcomes.

scholarly journals Artificial Joint Speed Feedback for Myoelectric Prosthesis Controls

2020 ◽  
Author(s):  
Eric J. Earley ◽  
Reva E. Johnson ◽  
Jonathon W. Sensinger ◽  
Levi J. Hargrove
Keyword(s):  
Sensory Feedback ◽  
Feedforward Control ◽  
Feedback System ◽  
Modest Improvement ◽  
Myoelectric Prosthesis ◽  
Reaching Task ◽  
Prosthesis Control ◽  
High Uncertainty ◽  
Relevant Factors ◽  
Positional Control

I.AbstractAccurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty – joint speed. In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found modest improvement in overall reaching errors after perturbed control, and that high prosthesis control noise was compensated for by strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

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