Data captured using low-cost active electromyography
Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in “Low-cost active electromyography” [1]. Data was captured for 45 seconds per subject, resulting in 12–21 contractions per subject. The raw data files, alo...
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| Veröffentlicht in: | Data in brief Jg. 29; S. 105239 |
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01.04.2020
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| Abstract | Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in “Low-cost active electromyography” [1]. Data was captured for 45 seconds per subject, resulting in 12–21 contractions per subject. The raw data files, along with a sinusoidal waveform have been provided. This allows users of the POLE sensor to verify their low-cost sEMG device has been populated and configured correctly. This data also allows researchers/developers to compare their results against this low-cost, low noise sEMG device. The frequency content of the raw sEMG data is also of interest; this is calculated by applying a fast Fourier transform (FFT). The process applied to perform these algorithms is supplied in a MATLAB script. |
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| AbstractList | Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in “Low-cost active electromyography” [1]. Data was captured for 45 seconds per subject, resulting in 12–21 contractions per subject. The raw data files, along with a sinusoidal waveform have been provided. This allows users of the POLE sensor to verify their low-cost sEMG device has been populated and configured correctly. This data also allows researchers/developers to compare their results against this low-cost, low noise sEMG device. The frequency content of the raw sEMG data is also of interest; this is calculated by applying a fast Fourier transform (FFT). The process applied to perform these algorithms is supplied in a MATLAB script. Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in "Low-cost active electromyography" [1]. Data was captured for 45 seconds per subject, resulting in 12-21 contractions per subject. The raw data files, along with a sinusoidal waveform have been provided. This allows users of the POLE sensor to verify their low-cost sEMG device has been populated and configured correctly. This data also allows researchers/developers to compare their results against this low-cost, low noise sEMG device. The frequency content of the raw sEMG data is also of interest; this is calculated by applying a fast Fourier transform (FFT). The process applied to perform these algorithms is supplied in a MATLAB script.Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in "Low-cost active electromyography" [1]. Data was captured for 45 seconds per subject, resulting in 12-21 contractions per subject. The raw data files, along with a sinusoidal waveform have been provided. This allows users of the POLE sensor to verify their low-cost sEMG device has been populated and configured correctly. This data also allows researchers/developers to compare their results against this low-cost, low noise sEMG device. The frequency content of the raw sEMG data is also of interest; this is calculated by applying a fast Fourier transform (FFT). The process applied to perform these algorithms is supplied in a MATLAB script. Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in “Low-cost active electromyography” [1]. Data was captured for 45 seconds per subject, resulting in 12–21 contractions per subject. The raw data files, along with a sinusoidal waveform have been provided. This allows users of the POLE sensor to verify their low-cost sEMG device has been populated and configured correctly. This data also allows researchers/developers to compare their results against this low-cost, low noise sEMG device. The frequency content of the raw sEMG data is also of interest; this is calculated by applying a fast Fourier transform (FFT). The process applied to perform these algorithms is supplied in a MATLAB script. Keywords: sEMG, EMG, Rehabilitation, Right leg driver, Bar electrodes, Bio-sensing, Bio-instrumentation |
| ArticleNumber | 105239 |
| Author | Fortune, Benjamin C. Pretty, Christopher G. Chatfield, Logan T. McKenzie, Lachlan R. Hayes, Michael P. |
| AuthorAffiliation | a Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand b Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand |
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| Keywords | Right leg driver Bio-sensing sEMG Bar electrodes Bio-instrumentation Rehabilitation EMG |
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| References | Fortune, Pretty, Chatfield, McKenzie, Hayes (bib1) Oct. 2019; 6 Analog Devices (bib2) 2018 Analog Devices (10.1016/j.dib.2020.105239_bib2) 2018 Fortune (10.1016/j.dib.2020.105239_bib1) 2019; 6 |
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| Snippet | Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in “Low-cost... Surface electromyography (sEMG) data was captured for three able-body subjects, from their right biceps brachii using the POLE sensor outlined in "Low-cost... |
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| StartPage | 105239 |
| SubjectTerms | algorithms Bar electrodes Bio-instrumentation Bio-sensing electromyography EMG Engineering Rehabilitation researchers Right leg driver sEMG supply surfaces |
| Title | Data captured using low-cost active electromyography |
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