Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Sensors
Jiheon KangDoo-Seop Eom

Abstract

We introduce a novel method for indoor localization with the user's own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all...Continue Reading

References

Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
Aug 19, 2015·IEEE Transactions on Bio-medical Engineering·Serkan KiranyazMoncef Gabbouj
May 20, 2016·Sensors·Abdulrahman AlarifiHend S Al-Khalifa
Jan 20, 2017·IEEE Journal of Biomedical and Health Informatics·Julius HanninkBjoern M Eskofier
Mar 24, 2017·IEEE Journal of Biomedical and Health Informatics·Julius HanninkBjoern M Eskofier
Oct 11, 2017·Neural Networks : the Official Journal of the International Neural Network Society·German I ParisiStefan Wermter

❮ Previous
Next ❯

Methods Mentioned

BETA
feature extraction
feature extractions

Software Mentioned

TensorFlow
Galaxy S7
TensorFlow Mobile
Nougat
Android app
DeepSense

Related Concepts

Related Feeds

Aphasia

Aphasia affects the ability to process language, including formulation and comprehension of language and speech, as well as the ability to read or write. Here is the latest research on aphasia.