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sEMG Delsys System Delsys Electrodes Placement

Classifying Surface EMG Signals for Early Detection of Vocal Fatigue in Student Teachers.

The goal of the project is to determine the validity and reliability of a novel sEMG pattern recognition system to detect vocal fatigue.

Investigators

ViGIR Lab and MUIDSI

School of Health Professions

Funding

National Institute on Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health under Award Number R15DC015335

Data

NIH sEMG data sample

Placement of the electrodes for data collection

Laryngeal sEMG dataset1

This dataset includes sEMG signals from submental and infrahyoid muscle groups during a sequence of voice, speech, and non-speech productions. Acoustic signals were recorded simultaneously. Signals were collected from 61 female participants (teachers and non-teachers) who were in good general health with varying degrees of vocal fatigue as measured by the Vocal Fatigue Index (Nanjundeswaran, Jacobson, Gartner-Schmidt, & Verdolini Abbott, 2015). Participants showed normal mid-membranous vocal fold closure during comfortable pitch and loudness based on laryngeal videostroboscopy. The study was approved by the Institutional Review Board of the University of Missouri. All participants gave informed consent and consented to be included in the database.

The dataset includes information on and can be filtered by:

Data Collection

The equipment was installed in a sound-attenuating booth (IAC Acoustics, North Aurora, IL) and included: a base station and four wireless TrignoTM Mini sEMG sensors with a built-in bandwidth of 20 Hz to 450 Hz (Delsys, Natick, MA); a head-worn microphone (AKG, Model C520, Vienna, Austria); an audio interface (Scarlett 2i2, Focusrite, High Wycombe, UK); and a data acquision device (PowerLab 16/35, ADInstruments, Dunedin, New Zealand), which performed synchronized sampling of audio and sEMG signals using LabChart for Windows. The sampling rate was set to 4 kHz for the sEMG signal and 20 kHz for audio, both with 16-bit quantization.

Voice Production Sequence1

Task Description Reps Time
baseline Neutral with no movements for collecting pure noise 1 2 secs
syllable1 "afa afa afa ifi ifi ifi ufu ufu ufu" 1 6 secs
/a/ normal /a/ as in honest 55 2 secs
/u/ normal /u/ as in you 55 2 secs
/i/ normal /i/ as in feel 55 2 secs
sentence1 "The dew shimmered over my shiny blue shell again" 55 4 secs
sentence2 "Only we feel you do fail in new fallen duw" 55 4 secs
syllable2 "afa afa afa ifi ifi ifi ufu ufu ufu" 1 6 secs
/a/ pressed /a/ with breath hold 55 2 secs
throat clear Single throat clear 55 1.5 secs
/u/ pressed /u/ with breath hold 55 2 secs
cough Single cough 55 1.5 secs
/i/ pressed /i/ with breath hold 55 2 secs
syllable3 "afa afa afa ifi ifi ifi ufu ufu ufu" 1 6 secs
100% MVC Maximum voluntary contraction 3 8 secs
50% MVC Submaximal voluntary contraction 3 15 secs

1 Y. Gao, M. Dietrich, M. Pfeiffer and G. N. DeSouza, "Classification of sEMG Signals for the Detection of Vocal Fatigue based on VFI Scores," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 5014-5017

Downloading

We appreciate your interest in our research. By downloading the sEMGv1 dataset, you and/or your research group are agreeing to cite our sEMG dataset and the associated paper(s) in any form of publication produced by you/your group using the same dataset.

Y. Gao, G. N. DeSouza, and M. Dietrich. Laryngeal sEMG Dataset, v.1, Columbia, MO: University of Missouri, Department of Electrical Engineering and Computer Science and Department of Speech, Language and Hearing Sciences, 2020. http://vigir.missouri.edu/Research/sEMG_dataset.htm

Y. Gao, M. Dietrich, M. Pfeiffer and G. N. DeSouza, "Classification of sEMG Signals for the Detection of Vocal Fatigue based on VFI Scores," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 5014-5017. doi: 10.1109/EMBC.2018.8513224

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