A touch interface exploiting time-frequency classification using zak transform for source localization on solids

We propose a new approach to the development of a touch interface using surface-mounted sensors which allows one to convert a hard surface into a touch pad. This is achieved by using location template matching (LTM), a source localization algorithm that is robust to dispersion and multipath. In this interdisciplinary research, we employ mechanical vibration theories that model wave propagation of the flexural modes of vibration generated by an impact on the surface. We then verify that the amplitude variance across time for each propagating mode frequency is unique to each location on a surface. We show that the Zak transform allows us to faithfully track these amplitude variations and we exploit the uniqueness of this variance as a time-frequency classifier which in turn allows us to localize a finger tap in the context of a human-computer interface. The performance of the proposed algorithm is compared with existing LTM approaches on real surfaces.