Ultrasonic Based Proximity Detection for Handsets

Abstract

A novel approach for proximity detection on mobile handsets which does not require any additional transducers is presented. The method is based on transmitting a chirp and processing the received signal by applying Least Mean Square (LMS), where the desired signal is the transmitted chirp. The envelope of three signals (estimated filter taps, estimated output and error signal) are characterized with a set of 12 features which are used to classify a given frame into one of two classes: proximity active or proximity inactive. The classifier employed is based on Support Vector Machine (SVM) with linear kernel. The results show that over 13 minutes of recorded data, the accuracy achieved is 95.28% using 10-fold cross-validation. Furthermore, the feature importance analysis performed on the database indicates that the most relevant feature is based on the estimated filter taps.