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Measuring the Switch Cost of Smartphone Use While Walking

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

Jiheon Kang¹, Joonbeom Lee², Doo-Seop Eom³

¹Department of Electrical Engineering, Korea University, Seoul 02841, Korea. kanghead@korea.ac.kr.

Sensors (Basel, Switzerland)

|September 21, 2018

Summary

This study presents a new smartphone-based indoor localization method using personalized walking patterns. It leverages a deep learning model to accurately estimate distance traveled, improving pedestrian dead reckoning (PDR) systems.

Keywords:

indoor localization smartphone-based pedestrian dead reckoning stride length estimation time-series signal deep learning framework

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Area of Science:

Computer Science
Robotics
Signal Processing

Background:

Traditional pedestrian dead reckoning (PDR) indoor localization relies on generalized formulas for step count and stride length.
Existing methods often use manually designed features from sensor signals, limiting adaptability and efficiency.
There is a need for more accurate and automated methods for indoor localization using readily available smartphone sensors.

Purpose of the Study:

To develop a novel indoor localization method using a user's smartphone.
To learn personalized walking patterns for improved distance estimation.
To reduce the cost and increase the efficiency of dataset construction for localization models.

Main Methods:

A hybrid multiscale convolutional and recurrent neural network (CNN-RNN) model was proposed to learn pedestrian velocity from segmented sensor signal frames.
Distance traveled is estimated by calculating velocity and elapsed time, moving beyond traditional step-based methods.
A real-time, automatic dataset construction approach was developed using synchronized inertial sensor and GPS data collected outdoors.

Main Results:

The proposed deep learning model achieved a low distance error of <2.4% and >1.5% in indoor experiments.
The automatic dataset construction method significantly reduced costs and improved efficiency.
The model demonstrated applicability to various time-series sensor signal processing tasks.

Conclusions:

The novel hybrid CNN-RNN approach enables accurate indoor localization by learning personalized walking dynamics.
The method offers a cost-effective and efficient alternative for dataset creation compared to manual methods.
This deep learning-based strategy enhances the performance of smartphone-based indoor positioning systems.