Who is wearing me? TinyDL‐based user recognition in constrained personal devices
Abstract Deep learning (DL) techniques have been extensively studied to improve their precision and scalability in a vast range of applications. Recently, a new milestone has been reached driven by the emergence of the TinyDL paradigm, which enables adaptation of complex DL models generated by well‐...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | IET Computers & Digital Techniques |
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| Online Access: | https://doi.org/10.1049/cdt2.12035 |
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| author | Ramon Sanchez‐Iborra Antonio Skarmeta |
| author_facet | Ramon Sanchez‐Iborra Antonio Skarmeta |
| author_sort | Ramon Sanchez‐Iborra |
| collection | DOAJ |
| description | Abstract Deep learning (DL) techniques have been extensively studied to improve their precision and scalability in a vast range of applications. Recently, a new milestone has been reached driven by the emergence of the TinyDL paradigm, which enables adaptation of complex DL models generated by well‐known libraries to the restrictions of constrained microcontroller‐based devices. In this work, a comprehensive discussion is provided regarding this novel ecosystem, by identifying the benefits that it will bring to the wearable industry and analysing different TinyDL initiatives promoted by tech giants. The specific use case of automatic user recognition from data captured by a wearable device is also presented. The whole development process by which different DL configurations have been embedded in a real microcontroller unit is described. The attained results in terms of accuracy and resource usage confirm the validity of the proposal, which allows precise predictions in a highly constrained platform with limited input information. Therefore, this work provides insights into the viability of the integration of TinyDL models within wearables, which may be valuable for researchers, practitioners, and makers related to this industry. |
| format | Article |
| id | doaj-art-ba5160b66f1c4b57bd729db0d2763ab4 |
| institution | Kabale University |
| issn | 1751-8601 1751-861X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computers & Digital Techniques |
| spelling | doaj-art-ba5160b66f1c4b57bd729db0d2763ab42025-02-03T06:47:34ZengWileyIET Computers & Digital Techniques1751-86011751-861X2022-01-011611910.1049/cdt2.12035Who is wearing me? TinyDL‐based user recognition in constrained personal devicesRamon Sanchez‐Iborra0Antonio Skarmeta1Department of Engineering and Applied Techniques University Center of Defense Madrid SpainDepartment of Information Engineering and Communications University of Murcia Murcia SpainAbstract Deep learning (DL) techniques have been extensively studied to improve their precision and scalability in a vast range of applications. Recently, a new milestone has been reached driven by the emergence of the TinyDL paradigm, which enables adaptation of complex DL models generated by well‐known libraries to the restrictions of constrained microcontroller‐based devices. In this work, a comprehensive discussion is provided regarding this novel ecosystem, by identifying the benefits that it will bring to the wearable industry and analysing different TinyDL initiatives promoted by tech giants. The specific use case of automatic user recognition from data captured by a wearable device is also presented. The whole development process by which different DL configurations have been embedded in a real microcontroller unit is described. The attained results in terms of accuracy and resource usage confirm the validity of the proposal, which allows precise predictions in a highly constrained platform with limited input information. Therefore, this work provides insights into the viability of the integration of TinyDL models within wearables, which may be valuable for researchers, practitioners, and makers related to this industry.https://doi.org/10.1049/cdt2.12035deep learningIoTTinyDLwearables |
| spellingShingle | Ramon Sanchez‐Iborra Antonio Skarmeta Who is wearing me? TinyDL‐based user recognition in constrained personal devices IET Computers & Digital Techniques deep learning IoT TinyDL wearables |
| title | Who is wearing me? TinyDL‐based user recognition in constrained personal devices |
| title_full | Who is wearing me? TinyDL‐based user recognition in constrained personal devices |
| title_fullStr | Who is wearing me? TinyDL‐based user recognition in constrained personal devices |
| title_full_unstemmed | Who is wearing me? TinyDL‐based user recognition in constrained personal devices |
| title_short | Who is wearing me? TinyDL‐based user recognition in constrained personal devices |
| title_sort | who is wearing me tinydl based user recognition in constrained personal devices |
| topic | deep learning IoT TinyDL wearables |
| url | https://doi.org/10.1049/cdt2.12035 |
| work_keys_str_mv | AT ramonsancheziborra whoiswearingmetinydlbaseduserrecognitioninconstrainedpersonaldevices AT antonioskarmeta whoiswearingmetinydlbaseduserrecognitioninconstrainedpersonaldevices |