Next Article in Journal
Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications
Next Article in Special Issue
Smart Collaborative Caching for Information-Centric IoT in Fog Computing
Previous Article in Journal
Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose
Previous Article in Special Issue
Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(10), 2377; doi:10.3390/s17102377

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
*
Authors to whom correspondence should be addressed.
Received: 1 September 2017 / Revised: 22 September 2017 / Accepted: 3 October 2017 / Published: 18 October 2017
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
View Full-Text   |   Download PDF [1304 KB, uploaded 18 October 2017]   |  

Abstract

We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates. View Full-Text
Keywords: indoor localization; activities of daily living; activity recognition; sensor fusion; passive infrared (PIR) sensors; Bluetooth Low-Energy (BLE); BLE beacons; Estimote; anonymous sensing; eponymous sensing indoor localization; activities of daily living; activity recognition; sensor fusion; passive infrared (PIR) sensors; Bluetooth Low-Energy (BLE); BLE beacons; Estimote; anonymous sensing; eponymous sensing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mohebbi, P.; Stroulia, E.; Nikolaidis, I. Sensor-Data Fusion for Multi-Person Indoor Location Estimation. Sensors 2017, 17, 2377.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top