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Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies.

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Titel: Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies.
Autoren: Mattersberger, Mathias1, Wachtler, Josef1, Ebner, Martin1 mebner@gmx.at
Quelle: International Journal of Interactive Mobile Technologies. 2024, Vol. 18 Issue 10, p59-70. 12p.
Schlagwörter: Deep learning, Mobile learning, Object recognition (Computer vision), Web-based user interfaces, Two-dimensional bar codes
Abstract: This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies. [ABSTRACT FROM AUTHOR]
Datenbank: Supplemental Index
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  Data: Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies.
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  Data: <searchLink fieldCode="AR" term="%22Mattersberger%2C+Mathias%22">Mattersberger, Mathias</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wachtler%2C+Josef%22">Wachtler, Josef</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ebner%2C+Martin%22">Ebner, Martin</searchLink><relatesTo>1</relatesTo><i> mebner@gmx.at</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2024, Vol. 18 Issue 10, p59-70. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Two-dimensional+bar+codes%22">Two-dimensional bar codes</searchLink>
– Name: Abstract
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  Data: This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3991/ijim.v18i10.49037
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 59
    Subjects:
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Mobile learning
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Web-based user interfaces
        Type: general
      – SubjectFull: Two-dimensional bar codes
        Type: general
    Titles:
      – TitleFull: Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies.
        Type: main
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            NameFull: Mattersberger, Mathias
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            NameFull: Wachtler, Josef
      – PersonEntity:
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            NameFull: Ebner, Martin
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            – D: 15
              M: 05
              Text: 2024
              Type: published
              Y: 2024
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              Value: 18
            – Type: issue
              Value: 10
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            – TitleFull: International Journal of Interactive Mobile Technologies
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