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Sensor-Data Fusion for Multi-Person Indoor Location Estimation.

Parisa Mohebbi¹, Eleni Stroulia², Ioanis Nikolaidis³

¹Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada. mohebbi@ualberta.ca.

Sensors (Basel, Switzerland)

|October 24, 2017

Summary

This summary is machine-generated.

This study develops a sensor fusion method for indoor localization, combining anonymous motion sensors and wearable devices. It effectively tracks individuals, even when they cannot carry smartphones, improving location accuracy in daily living activities.

Keywords:

BLE beacons Bluetooth Low-Energy (BLE)Estimote activities of daily living activity recognition anonymous sensing eponymous sensing indoor localization passive infrared (PIR) sensors sensor fusion

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Area of Science:

Ubiquitous Computing
Human-Computer Interaction
Sensor Networks

Background:

Indoor localization is crucial for monitoring people's movement and activities.
Tracking individuals who cannot carry wearable devices, such as in caregiving scenarios, presents unique challenges.
Existing methods often rely on wearable sensors, limiting applicability.

Purpose of the Study:

To propose a sensor fusion solution for indoor localization.
To address scenarios where individuals cannot or will not carry smartphones.
To evaluate the effectiveness of combining anonymous and eponymous sensors for location estimation.

Main Methods:

Utilized anonymous Passive Infrared (PIR) motion sensors.
Employed eponymous wearable sensors (smartphones with Estimote beacons).
Developed a sensor-fusion approach to integrate data from disparate sources.

Main Results:

Investigated the performance of anonymous-only, eponymous-only, and combined sensor strategies.
Analyzed the impact of sensor data synchronization on location estimate quality.
Examined the trade-off between sensor coverage and localization accuracy.

Conclusions:

Combining anonymous and eponymous sensors offers a robust solution for indoor localization.
Synchronization issues can be mitigated without device-level mechanisms.
Sensor coverage and fusion strategies are key to accurate location estimation in complex environments.