Echo-ID: Smartphone Placement Region Identification for Context-Aware Computing

Abstract

Region-function combinations are essential for smartphones to be intelligent and context-aware. The prerequisite for providing intelligent services is that the device can recognize the contextual region in which it resides. The existing region recognition schemes are mainly based on indoor positioning, which require pre-installed infrastructures or tedious calibration efforts or memory burden of precise locations. In addition, location classification recognition methods are limited by either their recognition granularity being too large (room-level) or too small (centimeter-level, requiring training data collection at multiple positions within the region), which constrains the applications of providing contextual awareness services based on region function combinations. In this paper, we propose a novel mobile system, called Echo-ID, that enables a phone to identify the region in which it resides without requiring any additional sensors or pre-installed infrastructure. Echo-ID applies Frequency Modulated Continuous Wave (FMCW) acoustic signals as its sensing medium which is transmitted and received by the speaker and microphones already available in common smartphones. The spatial relationships among the surrounding objects and the smartphone are extracted with a signal processing procedure. We further design a deep learning model to achieve accurate region identification, which calculate finer features inside the spatial relations, robust to phone placement uncertainty and environmental variation. Echo-ID requires users only to put their phone at two orthogonal angles for 8.5 s each inside a target region before use. We implement Echo-ID on the Android platform and evaluate it with Xiaomi 12 Pro and Honor-10 smartphones. Our experiments demonstrate that Echo-ID achieves an average accuracy of 94.6% for identifying five typical regions, with an improvement of 35.5% compared to EchoTag. The results confirm Echo-ID’s robustness and effectiveness for region identification.

Keywords

• ultrasound sensing
• region identification
• deep learning