Comparison of six electromyography acquisition setups on hand movement classification tasks

Abstract

Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition setups are based on four different sEMG electrodes (including Otto Bock, Delsys Trigno, Cometa Wave + Dormo ECG and two Thalmic Myo armbands) and they were used to record more than 50 hand movements from intact subjects with a standardized acquisition protocol. The relative performance of the six sEMG acquisition setups is compared on 41 identical hand movements with a standardized feature extraction and data analysis pipeline aimed at performing hand movement classification. Comparable classification results are obtained with three acquisition setups including the Delsys Trigno, the Cometa Wave and the affordable setup composed of two Myo armbands. The results suggest that practical sEMG tests can be performed even when costs are relevant (e.g. in small laboratories, developing countries or use by children). All the presented datasets can be used for offline tests and their quality can easily be compared as the data sets are publicly available.

Fig 1. Acquisition setups for DB1 Otto Bock 13E200 (a), DB2 Delsys Trigno (b), DB4 (Cometa+Dormo) (c) and DB5 (Double Myo) (d).

Fig 2. Software system including NinaPro software, MultiEmgDevice, and MyoSocket.

The scheme shows how the applications interact with each other and with the external devices.

Fig 3. Signal and spectrum of exercise 1, with different setups from DB1 (Otto-Bock), DB2 (Delsys Trigno), DB4 (Cometa+Dormo), DB5 (Double Myo).

The 50Hz band is highlighted.

Fig 4. sEMG signal amplitude analysis.

Characterization of four Ninapro datasets. The first two rows represent the datasets discussed in this paper (Cometa+Dormo and Myo datasets), while the last two rows represent an analysis made on a subset of subject from the Otto-Bock and the Delsys Trigno datasets, as explained in subsection 2.1. The first column represents the variability of the EMG signal on the 6 repetitions, considering all movements and subjects. The second column shows the variability of different movements, considering all the subjects and all repetitions. The third column represents the variability of the signal in each subject, considering all movements and repetitions. The horizontal central mark in the boxes is the median; the edges of the boxes are the 25th and 75th percentiles; the whiskers extend to approximately 2.7 times the standard deviation.

Fig 5. Acquisition setup comparison on the classification of 41 hand movements with (A) a Random Forests classifier and (B) SVM.