We propose an algorithm to localize impacts on rigid surfaces using induced vibration signals. This allows for the conversion of daily objects, such as tabletops and glass panels, into human–computer touch interfaces using low-cost piezoelectric sensors. Impact localization is achieved by estimating the time-of-arrivals and subsequently time-difference-of-arrivals of the sensor-received signals. Time-of-arrival estimation is highly challenging with increasing source–sensor distance due to the occurrence of a gradual noise-to-signal transition at the sensor output. We address this problem by first converting the signal into Hermitian angle distributions. The time-varying probability contributions of the background noise and vibration signal in each of the distributions are subsequently monitored to identify the instant when the signal begins to dominate the noise, signifying the signals arrival. The proposed framework also allows simultaneous time-of-arrival estimation across all the sensors to minimize errors in the resultant time-difference-of-arrival estimates. Experimental results show that the proposed algorithm outperforms existing techniques for source localization on solid surfaces of different materials.

We propose a TDOA-based algorithm for source localization on rigid surfaces. This allows the conversion of readily available large surfaces into touch interfaces using surface-mounted vibration sensors. To achieve this, we characterize the arrival of each sensor-received signal by the arrival times of its frequency components. To estimate the arrival time of each frequency component, we first model each component as a harmonic random process. A kurtosis sequence, which exhibits a sharp rising edge when the signal begins to deviate from the background noise, can then be obtained for each component. By accurately estimating the starting point of the rising edge, our algorithm can avoid the uncertainty due to gradual noise-to-signal transition. Experiment results show that the proposed algorithm achieves better localization performance than existing techniques on large surfaces.

We propose a new algorithm for source localization on rigid surfaces, which allows one to convert daily objects into human-computer touch interfaces using surface-mounted vibration sensors. This is achieved via estimating the time-difference-of-arrivals (TDOA) of the signals across the sensors. In this work, we employ a smooth parametrized function to model the gradual noise-to-signal energy transition at each sensor. Specifically, the noise-to-signal transition is modeled by a four-parameter logistic function. The TDOA is then estimated as the difference in time shifts of the functions fitted to the sensor data. Experiment results show that the proposed algorithm significantly outperforms existing techniques which adopt the abrupt change model for time-of-arrival estimation.