scholarly journals Estimation of Targeted-Reaching-Positions by Around-Shoulder Muscle Activities and Images from an Action Camera for Trans-Humeral Prosthesis Control

Prosthesis ◽  
2020 ◽  
Author(s):  
Yohei Muraguchi ◽  
Wenwei Yu
Keyword(s):  
Shoulder Muscle ◽  
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.

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

scholarly journals Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life

2020 ◽  
Vol 14 ◽  
Author(s):  
Janne M. Hahne ◽  
Meike A. Wilke ◽  
Mario Koppe ◽  
Dario Farina ◽  
Arndt F. Schilling
Keyword(s):  
Daily Life ◽  
Longitudinal Case Study ◽  
Hand Prosthesis ◽  
Prosthesis Control

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

Quantifying scapula orientation and its influence on maximal hand force capability and shoulder muscle activity

2010 ◽  
Vol 25 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Bryan R. Picco ◽  
Steven L. Fischer ◽  
Clark R. Dickerson
Keyword(s):  
Muscle Activity ◽  
Shoulder Muscle ◽  
Hand Force
Sign in / Sign up

Export Citation Format

Share Document