Details
Original language | English |
---|---|
Article number | 39 |
Journal | ACM Transactions on Computer-Human Interaction |
Volume | 30 |
Issue number | 3 |
Publication status | Published - 10 Jun 2023 |
Externally published | Yes |
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
- Computer Science(all)
- Human-Computer Interaction
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In: ACM Transactions on Computer-Human Interaction, Vol. 30, No. 3, 39, 10.06.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - SparseIMU
T2 - Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures
AU - Sharma, Adwait
AU - Salchow-Hömmen, Christina
AU - Mollyn, Vimal Suresh
AU - Nittala, Aditya Shekhar
AU - Hedderich, Michael A.
AU - Koelle, Marion
AU - Seel, Thomas
AU - Steimle, Jürgen
N1 - Funding Information: This work received funding from Bosch Research and from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 714797).
PY - 2023/6/10
Y1 - 2023/6/10
N2 - 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.
AB - 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.
KW - design tool
KW - Gesture recognition
KW - hand gestures
KW - imu
KW - objects
KW - sensor placement
UR - http://www.scopus.com/inward/record.url?scp=85158084148&partnerID=8YFLogxK
U2 - 10.1145/3569894
DO - 10.1145/3569894
M3 - Article
AN - SCOPUS:85158084148
VL - 30
JO - ACM Transactions on Computer-Human Interaction
JF - ACM Transactions on Computer-Human Interaction
SN - 1073-0516
IS - 3
M1 - 39
ER -