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Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform

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Název: Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform
Autoři: Earley, Eric, 1989, Chan, Nathaly Sanchez, Naber, Autumn, 1988, Mastinu, Enzo, 1987, Truong, Minh T.N., Ortiz Catalan, Max Jair, 1982
Zdroj: IEEE Access. 12:69350-69358
Témata: Microprogramming, Electrocardiography, Electrodes, Software, EMG, bioelectric signal, pattern recognition, Electroencephalography, data acquisition, Performance evaluation, Hardware, open source
Popis: Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around $150 USD. This device can enable researchers and clinicians to record bioelectric signals from able-bodied or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/541221
https://research.chalmers.se/publication/541221/file/541221_Fulltext.pdf
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  Data: Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform
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  Data: <searchLink fieldCode="AR" term="%22Earley%2C+Eric%22">Earley, Eric</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Chan%2C+Nathaly+Sanchez%22">Chan, Nathaly Sanchez</searchLink><br /><searchLink fieldCode="AR" term="%22Naber%2C+Autumn%22">Naber, Autumn</searchLink>, 1988<br /><searchLink fieldCode="AR" term="%22Mastinu%2C+Enzo%22">Mastinu, Enzo</searchLink>, 1987<br /><searchLink fieldCode="AR" term="%22Truong%2C+Minh+T%2EN%2E%22">Truong, Minh T.N.</searchLink><br /><searchLink fieldCode="AR" term="%22Ortiz+Catalan%2C+Max+Jair%22">Ortiz Catalan, Max Jair</searchLink>, 1982
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  Data: <i>IEEE Access</i>. 12:69350-69358
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  Data: <searchLink fieldCode="DE" term="%22Microprogramming%22">Microprogramming</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Electrodes%22">Electrodes</searchLink><br /><searchLink fieldCode="DE" term="%22Software%22">Software</searchLink><br /><searchLink fieldCode="DE" term="%22EMG%22">EMG</searchLink><br /><searchLink fieldCode="DE" term="%22bioelectric+signal%22">bioelectric signal</searchLink><br /><searchLink fieldCode="DE" term="%22pattern+recognition%22">pattern recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22data+acquisition%22">data acquisition</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Hardware%22">Hardware</searchLink><br /><searchLink fieldCode="DE" term="%22open+source%22">open source</searchLink>
– Name: Abstract
  Label: Description
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  Data: Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around $150 USD. This device can enable researchers and clinicians to record bioelectric signals from able-bodied or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices.
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        Value: 10.1109/ACCESS.2024.3397909
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      – SubjectFull: Microprogramming
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      – SubjectFull: Electrocardiography
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      – SubjectFull: Electrodes
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      – SubjectFull: Software
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      – SubjectFull: EMG
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      – SubjectFull: bioelectric signal
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      – SubjectFull: pattern recognition
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      – SubjectFull: Electroencephalography
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      – TitleFull: Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform
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              M: 01
              Type: published
              Y: 2024
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