scholarly journals Electromyography Pattern-Recognition-Based Control of Powered Multifunctional Upper-Limb Prostheses

10.5772/22876 ◽  
2011 ◽  
Author(s):  
Guanglin Li
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Upper Limb Prostheses

scholarly journals Users’ and therapists’ perceptions of myoelectric multi-function upper limb prostheses with conventional and pattern recognition control

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220899 ◽  
Author(s):  
Andreas W. Franzke ◽  
Morten B. Kristoffersen ◽  
Raoul M. Bongers ◽  
Alessio Murgia ◽  
Barbara Pobatschnig ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Upper Limb Prostheses

scholarly journals Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2402 ◽  
Author(s):  
Ali Al-Timemy ◽  
Guido Bugmann ◽  
Javier Escudero
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Time Domain ◽  
Window Size ◽  
Error Rates ◽  
Classification Error ◽  
Linear Discriminant ◽  
Emg Signal ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signal increases. We demonstrate our framework utilizing EMG datasets collected from nine transradial amputees who performed nine movement classes with Time Domain Power Spectral Descriptors (TD-PSD), Wavelet and Time Domain (TD) feature extraction (FE) methods and a Linear Discriminant Analysis (LDA) classifier. Nonetheless, the concept can be applied to other types of features and classifiers. In addition, the proposed framework is validated with different movement and EMG channel combinations. The results indicate that the proposed framework works well with different FE methods and movement/channel combinations with classification error rates of approximately 13% with TD-PSD FE. Thus, we expect our proposed framework to be a straightforward, yet important, step towards the improvement of the control methods for upper-limb prostheses.

Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation

2016 ◽  
Vol 24 (9) ◽  
pp. 961-970 ◽  
Author(s):  
Marina M.-C. Vidovic ◽  
Han-Jeong Hwang ◽  
Sebastian Amsuss ◽  
Janne M. Hahne ◽  
Dario Farina ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Upper Limb Prostheses

The Next Generation of Upper-Limb Prostheses, 1994

1994 ◽  
Author(s):  
William H. Donovan ◽  
◽  
Diane J. Atkins ◽  
Denise C. Y. Heard
Keyword(s):  
Upper Limb ◽  
Next Generation ◽  
Upper Limb Prostheses
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