Multi-Session Electrocardiogram–Electromyogram Database for User Recognition Article Swipe
Jin-Su Kim
,
Cheol Ho Song
,
Jae Myung Kim
,
Jimin Lee
,
Yeong-Hyeon Byeon
,
Jaehyo Jung
,
Hyun‐Sik Choi
,
Keun-Chang Kwak
,
Youn Tae Kim
,
EunSang Bak
,
Sungbum Pan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14062607
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14062607
Current advancements in biosignal-based user recognition technology are paving the way for a next-generation solution that addresses the limitations of face- and fingerprint-based user recognition methods. However, existing biosignal benchmark databases (DBs) for user recognition often suffer from limitations, such as data collection from a small number of subjects in a single session, hindering comprehensive analysis of biosignal variability. This study introduces CSU_MBDB1 and CSU_MBDB2, databases containing electrocardiogram (ECG) and electromyogram (EMG) signals from diverse experimental subjects recorded across multiple sessions. These in-house DBs comprise ECG and EMG data recorded in multiple sessions from 36 and 58 subjects, respectively, with a time interval of more than one day between sessions. During the experiments, subjects performed a total of six gestures while comfortably seated at a desk. CSU_MBDB1 and CSU_MBDB2 consist of three identical gestures, providing expandable data for various applications. When the two DBs are expanded, ECGs and EMGs from 94 subjects can be used, which is the largest number among the multi-biosignal benchmark DBs built by multi-sessions. To assess the usability of the constructed DBs, a user recognition experiment was conducted, resulting in an accuracy of 66.39% for ten subjects. It is important to emphasize that we focused on demonstrating the applicability of the constructed DBs using a basic neural network without signal denoising capabilities. While this approach results in a sacrifice in accuracy, it concurrently provides substantial opportunities for performance enhancement through the implementation of optimized algorithms. Adapting signal denoising processes to the constructed DBs and designing a more sophisticated neural network would undoubtedly contribute to improving the recognition accuracy. Consequently, these constructed DBs hold promise in user recognition, offering valuable research for future investigations. Additionally, DBs can be used in research to analyze the nonlinearity characteristics of ECG and EMG.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14062607
- https://www.mdpi.com/2076-3417/14/6/2607/pdf?version=1710995876
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393025355
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393025355Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app14062607Digital Object Identifier
- Title
-
Multi-Session Electrocardiogram–Electromyogram Database for User RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-20Full publication date if available
- Authors
-
Jin-Su Kim, Cheol Ho Song, Jae Myung Kim, Jimin Lee, Yeong-Hyeon Byeon, Jaehyo Jung, Hyun‐Sik Choi, Keun-Chang Kwak, Youn Tae Kim, EunSang Bak, Sungbum PanList of authors in order
- Landing page
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https://doi.org/10.3390/app14062607Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/6/2607/pdf?version=1710995876Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/14/6/2607/pdf?version=1710995876Direct OA link when available
- Concepts
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Biosignal, Computer science, Session (web analytics), Benchmark (surveying), Usability, Speech recognition, Artificial intelligence, Pattern recognition (psychology), Human–computer interaction, Computer vision, World Wide Web, Geography, Geodesy, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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