scholarly journals Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning

2018 ◽  
Vol 65 (4) ◽  
pp. 770-778 ◽  
Author(s):  
Joseph L. Betthauser ◽  
Christopher L. Hunt ◽  
Luke E. Osborn ◽  
Matthew R. Masters ◽  
Gyorgy Levay ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Sparse Representations ◽  
Myoelectric Prosthesis ◽  
Limb Position ◽  
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

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

scholarly journals An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7404
Author(s):  
Veronika Spieker ◽  
Amartya Ganguly ◽  
Sami Haddadin ◽  
Cristina Piazza
Keyword(s):  
Pattern Recognition ◽  
Position Effect ◽  
Adaptive Method ◽  
Completion Rate ◽  
Hand Motion ◽  
Limb Position ◽  
Upper Limb Prostheses ◽  
New Information ◽  
Classical Pattern ◽  
External Loads

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

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

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.

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