health life sciences medicine
This dataset is an extension of the Gesture Recognition and Biometrics Electromyogram (GRABMyo) dataset, a comprehensive collection of surface electromyography (sEMG) signals from the forearm and wrist while performing static gestures, designed for hand gesture recognition (HGR) and biometric authentication research. The study extends the dataset in two key aspects: 1) by adding wrist-only recordings from 20 newly recruited healthy adults, and 2) by including a dynamic trial at the end of each session. The extension study follows the protocol established in the original study to ensure methodological consistency and reliable data acquisition. Combined with prior wrist recordings, the extended dataset now includes data from 63 participants, significantly increasing the sample size. Signal strength and quality in the new recordings have been validated to match the standards of the original dataset, ensuring uniformity across all subjects. The dynamic gesture recordings included in the extension capture the natural transitions between hand poses, better simulating real-world movement scenarios. These dynamic data enable new avenues for continuous authentication and gesture-event segmentation, directly addressing prior limitations of static-only datasets. Data acquisition employed the established electrode configuration, and open access to the data in the waveform database (WFDB)-format is provided to support the widespread use of research. This enriched dataset enhances both the scale and temporal complexity of the GRABMyo dataset, delivering a robust resource for advancing sEMG-based HGR for human-computer interaction or biometric systems research.
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Creative Commons Attribution 4.0 International Public License
https://doi.org/10.13026/ah52-zq51
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GRABMyoFlow - Dataset extension was accessed on DATE from https://registry.opendata.aws/grabmyo-flow.
arn:aws:s3:::physionet-open/grabmyo-flow/us-east-1aws s3 ls --no-sign-request s3://physionet-open/grabmyo-flow/