Smartphone Motion Mode Recognition

Itzik Klein, Yuval Solaz, Guy Ohayon

Research output: Contribution to journalArticlepeer-review

Abstract

The possibility of using mobile devices, such as smartphones, for locating a person indoor is becoming more attractive for many applications. Among them are health care and safety services, commercial and emergency applications. One of the approaches to find the smartphone position is known as Pedestrian Dead Reckoning (PDR). PDR relies on the smartphone low-cost sensors, such as accelerometers, gyroscopes, barometer and magnetometers. An appropriate calibration phase to find the step length algorithm gains is required before PDR can be applied. These gains are very sensitive to the user and smartphone mode. In this research, we employ machine learning classifications algorithms in order to recognize and classify the pedestrian and smartphone modes. A methodology of training on a single user and testing on multiple users is proposed and experimentally evaluated Results show successes in classifying the user and smart phone modes.
Original languageEnglish
JournalProceedings
Volume2
Issue number3
DOIs
StatePublished - 2018
Externally publishedYes

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