Control of word processing environment using myoelectric signals


This paper shows how myoelectric signals (EMG) can be used to generate control signals for further use in human–machine interfaces. Our custom-built portable USB device is able to capture multi-channel highspeed surface EMG signals from muscles and its software counterpart is capable to control common PC interface including ordinary text editors such as MS Word. At the time of the study the system utilized three parallel EMG channels to control user interfaces. The interaction was based on series of 1-of-N selections which specify rows and columns in on-screen keyboards. The selection was performed by quantification of selected muscle activity of the user. The system was further tested by a disabled person who provided input during a participatory design session. Our study has demonstrated that the system and the user interface can be used for effective text input and editing also in disabled people.

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  1. 1.

    Thalmic Labs’ Myo is an example of commercially developed human interface device (HID) based on the EMG detection targeted on users with no disabilities to control their computer via gestures.

  2. 2.

    For instance Thalmic Labs’ Myo alpha version main purpose was to detect a set of discrete gestures rather than to provide a continuous stream of measurements at the time of the study. Current version offers 200 Hz data output.

  3. 3.

    The system allows up to 20 kHz.


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Correspondence to Adam J. Sporka.

Additional information

This work has been supported by (1) grant LH12070 (TextAble) awarded by the Ministry of Education, Youth and Sports of the Czech Republic, funding PROGRAM LH KONTAKT II, (2) grant SGS10/290/OHK3/3T/13 awarded by the CTU Prague, (3) grant P304/12/G069 awarded by the Czech Science Foundation, and (4) project AV0Z50110509 of the Academy of Sciences of the Czech Republic.

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Pošusta, A., Sporka, A.J., Poláček, O. et al. Control of word processing environment using myoelectric signals. J Multimodal User Interfaces 9, 299–311 (2015).

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  • Assistive technology
  • Text input
  • Myoelectric signals
  • User study