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SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Adwait Sharma
  • Christina Salchow-Hömmen
  • Vimal Suresh Mollyn
  • Aditya Shekhar Nittala
  • Thomas Seel

External Research Organisations

  • Saarland University
  • Charité - Universitätsmedizin Berlin
  • Technische Universität Berlin
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    • Citation Indexes: 8
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Details

Original languageEnglish
Article number39
JournalACM Transactions on Computer-Human Interaction
Volume30
Issue number3
Publication statusPublished - 10 Jun 2023
Externally publishedYes

Abstract

Gestural interaction with freehands and while grasping an everyday object enables always-available input. To sense such gestures, minimal instrumentation of the user's hand is desirable. However, the choice of an effective but minimal IMU layout remains challenging, due to the complexity of the multi-factorial space that comprises diverse finger gestures, objects, and grasps. We present SparseIMU, a rapid method for selecting minimal inertial sensor-based layouts for effective gesture recognition. Furthermore, we contribute a computational tool to guide designers with optimal sensor placement. Our approach builds on an extensive microgestures dataset that we collected with a dense network of 17 inertial measurement units (IMUs). We performed a series of analyses, including an evaluation of the entire combinatorial space for freehand and grasping microgestures (393 K layouts), and quantified the performance across different layout choices, revealing new gesture detection opportunities with IMUs. Finally, we demonstrate the versatility of our method with four scenarios.

Keywords

    design tool, Gesture recognition, hand gestures, imu, objects, sensor placement

ASJC Scopus subject areas

Cite this

SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures. / Sharma, Adwait; Salchow-Hömmen, Christina; Mollyn, Vimal Suresh et al.
In: ACM Transactions on Computer-Human Interaction, Vol. 30, No. 3, 39, 10.06.2023.

Research output: Contribution to journalArticleResearchpeer review

Sharma, A, Salchow-Hömmen, C, Mollyn, VS, Nittala, AS, Hedderich, MA, Koelle, M, Seel, T & Steimle, J 2023, 'SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures', ACM Transactions on Computer-Human Interaction, vol. 30, no. 3, 39. https://doi.org/10.1145/3569894
Sharma, A., Salchow-Hömmen, C., Mollyn, V. S., Nittala, A. S., Hedderich, M. A., Koelle, M., Seel, T., & Steimle, J. (2023). SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures. ACM Transactions on Computer-Human Interaction, 30(3), Article 39. https://doi.org/10.1145/3569894
Sharma A, Salchow-Hömmen C, Mollyn VS, Nittala AS, Hedderich MA, Koelle M et al. SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures. ACM Transactions on Computer-Human Interaction. 2023 Jun 10;30(3):39. doi: 10.1145/3569894
Sharma, Adwait ; Salchow-Hömmen, Christina ; Mollyn, Vimal Suresh et al. / SparseIMU : Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures. In: ACM Transactions on Computer-Human Interaction. 2023 ; Vol. 30, No. 3.
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abstract = "Gestural interaction with freehands and while grasping an everyday object enables always-available input. To sense such gestures, minimal instrumentation of the user's hand is desirable. However, the choice of an effective but minimal IMU layout remains challenging, due to the complexity of the multi-factorial space that comprises diverse finger gestures, objects, and grasps. We present SparseIMU, a rapid method for selecting minimal inertial sensor-based layouts for effective gesture recognition. Furthermore, we contribute a computational tool to guide designers with optimal sensor placement. Our approach builds on an extensive microgestures dataset that we collected with a dense network of 17 inertial measurement units (IMUs). We performed a series of analyses, including an evaluation of the entire combinatorial space for freehand and grasping microgestures (393 K layouts), and quantified the performance across different layout choices, revealing new gesture detection opportunities with IMUs. Finally, we demonstrate the versatility of our method with four scenarios.",
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