Name
Title | ||
|---|---|---|
Evaluating subsampling strategies for sEMG-based prediction of voluntary muscle contractions |
Abstract | ||
|---|---|---|
In previous work we showed that some human Voluntary Muscle Contractions (VMCs) of high interest to the prosthetics community, namely finger flexions/extensions and thumb rotation, can be effectively predicted using muscle activation signals coming from surface electromyography (sEMG). In this paper we study the effectiveness of various subsampling strategies to limit the size of the training data set, with the aim of extending the approach to an online VMC-prediction system whose main application will be force-controlled hand prostheses. We performed an experiment in which 10 able-bodied participants flexed and extended their fingers according to a visual stimulus, while muscle activations and VMCs (represented as synergistic fingertip forces) were gathered using sEMG electrodes and a custom-built measurement device. A Support Vector Machine (SVM) was trained on a fixed-sized subset of the collected data, obtained using seven different subsampling strategies. The SVM was then tested on subsequent new data. Our experimental results show that two subsampling strategies attain a prediction error as low as 6% to 12%, which is comparable to the error values obtained in our previous work when the entire data set was used and processed offline. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICORR.2013.6650492 | ICORR |
Keywords | Field | DocType |
electromyography,medical signal processing,prediction theory,prosthetics,signal sampling,support vector machines,custom built measurement device,finger extensions,finger flexions,force controlled hand prosthesis,human voluntary muscle contraction,muscle activation signals,prosthetics community,semg-based prediction,subsampling strategy,support vector machine,surface electromyography,thumb rotation,training data set,visual stimulus,voluntary muscle contraction,indexes,force,electrodes | Training set,Mean squared prediction error,Thumb,Support vector machine,Electromyography,Muscle activation,Speech recognition,Engineering | Conference |
ISSN | ISBN | Citations |
1945-7898 | 978-1-4673-6022-7 | 1 |
PageRank | References | Authors |
0.37 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Risto Kõiva | 1 | 67 | 8.00 |
| Hilsenbeck, B. | 2 | 1 | 0.37 |
| Claudio Castellini | 3 | 448 | 33.56 |