Directional Forgetting for Stable Co-Adaptation in Myoelectric Control
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2019-05-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
Series
Sensors (Basel, Switzerland), Volume 19, issue 9
Abstract
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.Description
| openaire: EC/H2020/687795/EU//INPUT
Keywords
co-adaptation, directional forgetting, electromyography, myoelectric control, upper-limb prostheses
Other note
Citation
Yeung, D, Farina, D & Vujaklija, I 2019, ' Directional Forgetting for Stable Co-Adaptation in Myoelectric Control ', Sensors (Basel, Switzerland), vol. 19, no. 9, 2203 . https://doi.org/10.3390/s19092203