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.