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 (393K 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.
Adwait Sharma, Christina Salchow-Hömmen, Vimal Suresh Mollyn, Aditya Shekhar Nittala, Michael A. Hedderich, Marion Koelle, Thomas Seel, Jürgen Steimle. SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-Grained Finger Microgestures. In undefined (TOCHI '22). Page: 1-40. DOI: https://doi.org/10.1145/3569894