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A deep adaptive framework for robust myoelectric hand movement prediction

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conference contribution
posted on 15.03.2019 by Carl Robinson, Baihua Li, Qinggang Meng, Matthew Pain
This work explored the requirements of accurately and reliably predicting user intention using a deep learning methodology when performing fine-grained movements of the human hand. The focus was on combining a feature engineering process with the effective capability of deep learning to further identify salient characteristics from a biological input signal. 3 time domain features (root mean square, waveform length, and slope sign changes) were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements performed by 40 subjects. The feature data was mapped to 6 sensor bend resistance readings from a CyberGlove II system, representing the associated hand kinematic data. These sensors were located at specific joints of interest on the human hand (the thumb’s metacarpophalangeal joint, the proximal interphalangeal joint of each finger, and the radiocarpal joint of the wrist). All datasets were taken from database 2 of the NinaPro online database repository. A 3-layer long short-term memory model with dropout was developed to predict the 6 glove sensor readings using a corresponding sEMG feature vector as input. Initial results from trials using test data from the 40 subjects produce an average mean squared error of 0.176. This indicates a viable pathway to follow for this prediction method of hand movement data, although further work is needed to optimize the model and to analyze the data with a more detailed set of metrics.

History

School

  • Sport, Exercise and Health Sciences

Published in

UK Robotics and Autonomous Systems, UK-RAS 2019.

Citation

ROBINSON, C.P. ... et al., 2019. A deep adaptive framework for robust myoelectric hand movement prediction. Presented at the 2nd UK Robotics and Autonomous Systems Conference, (UK-RAS 2019), Loughborough University, 24th January.

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

17/01/2019

Publication date

2019

Notes

This is a conference paper.

Language

en

Location

Loughborough

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