Determining Smartphone’s Placement Through Material Detection, Using Multiple Features Produced in Sound Echoes | Zendy

Tatsuhito Hasegawa | Zendy; Satoshi Hirahashi | Zendy; Makoto Koshino | Zendy

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Determining Smartphone’s Placement Through Material Detection, Using Multiple Features Produced in Sound Echoes

Author(s) -

Tatsuhito Hasegawa,

Satoshi Hirahashi,

Makoto Koshino

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2687467

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper proposes a system to determine the placement of a smartphone by using the acoustic properties of the surface materials nearby. Detecting the surrounding materials allows the smartphone to change its notification method automatically, based on situational factors. Researchers have studied how to recognize the position in which smartphones are worn while walking, using accelerometer data; however, it is difficult to identify a smartphone's position while stationary, because the accelerometer value does not change significantly when the smartphone is put down. In this paper, we developed a method to recognize surface materials close to a smartphone, using echoes; this method is based on the assumption that echoes of a selected frequency will differ in their properties, depending on the smartphone's placement and the surface materials nearby. Through our experiment, we found that our proposed method can classify 12 kinds of placement with 82.1% accuracy.

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