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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37188

Title: A deep adaptive framework for robust myoelectric hand movement prediction
Authors: Robinson, Carl Peter
Li, Baihua
Meng, Qinggang
Pain, Matthew T.G.
Issue Date: 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.
Abstract: 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.
Description: This paper is in closed address.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/37188
Publisher Link: https://www.ukras.org/wp-content/uploads/2019/03/UKRAS19-Proceedings-Final.pdf
Appears in Collections:Closed Access (Sport, Exercise and Health Sciences)
Closed Access (Computer Science)

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