Imperial College London
Portrait Picture

ProfessorDarioFarina

Faculty of Engineering, Department of Bioengineering

Chair in Neurorehabilitation Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 1387d.farina Website

 
 
//

Location

 

RSM 4.15Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Yeung:2019:10.3390/s19092203,
author = {Yeung, D and Farina, D and Vujaklija, I},
doi = {10.3390/s19092203},
journal = {Sensors (Basel)},
title = {Directional Forgetting for Stable Co-Adaptation in Myoelectric Control.},
url = {http://dx.doi.org/10.3390/s19092203},
volume = {19},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.
AU  - Yeung,D
AU  - Farina,D
AU  - Vujaklija,I
DO  - 10.3390/s19092203
PY  - 2019///
TI  - Directional Forgetting for Stable Co-Adaptation in Myoelectric Control.
T2  - Sensors (Basel)
UR  - http://dx.doi.org/10.3390/s19092203
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31086045
VL  - 19
ER  -
Download