Extended touch mobile user interfaces through sensor fusion

This article explores an efficient sensor fusion algorithm for detecting and classifying user taps on any neighboring surface even in the presence of various background acoustics. The fusion algorithm employs a tier classifier combining microphone and accelerometer detection of user taps on iOS platform resulting into 100%success rate for the datasets studied in this paper. Fusion of these two sensors eliminates the need for any added filtering, knowledge of precise sensor positioning or use of any specialized piezoelectric sensors, as has been done in past research, as well as gives a robust classification with high success-rate even as the signal to noise ratio significantly degrades.