Transferring Interference Into Reference: A Finger-input Approach Using Acoustic Signals

Acoustic sensing holds great promise in the realm of human-computer interaction owing to the ubiquitous availability of speakers and microphones in smart homes. In this work, we propose a promising solution aided by contactless acoustic sensing, where people with motor neuron disease (MND) who have lost their motion and speech capabilities can input text with only a signal finger. However, it is still challenging for finger sensing due to the tiny signal variation and severe interference from uncontrolled body motion or other objects. To address these issues, we propose a search-based strategy to extract tiny motions and an adaptive origin transformation method to eliminate the interference. Extensive experiments demonstrate that it can achieve a recognition accuracy of 96.2% for single-finger text inputting.