Thumb-to-finger interactions leverage the thumb for precise, eyes-free input with high sensory bandwidth. While previous research explored gestures based on touch contact and finger movement on the skin, interactions leveraging depth such as pressure and hovering input are still underinvestigated. We present MicroPress, a proof-of-concept device that can detect both, precise thumb pressure applied on the skin and hover distance between the thumb and the index finger. We rely on a wearable IMU sensor array and a bi-directional RNN deep learning approach to enable fine-grained control while preserving the natural tactile feedback and touch of the skin. We demonstrate MicroPress' efficacy with two interactive scenarios that pose challenges for real-time input and we validate its design with a study involving eight participants. With short per user calibration steps, MicroPress is capable of predicting hover distance with 0.57mm accuracy, and on-skin pressure with 6.71% normalized pressure error at 6 locations on the index finger.