ArticleSensors (Basel, Switzerland)2019
Multi-Sensor Fusion Approach for Improving Map-Based Indoor Pedestrian Localization.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A Large Language Model-Guided IMU-PDR Method for Detection and Error Correction.Sensors (Basel, Switzerland) · 2026Article
- Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments.Sensors (Basel, Switzerland) · 2024Article
- Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces.Sensors (Basel, Switzerland) · 2023Review
- Smartphone-Based Localization for Passengers Commuting in Traffic Hubs.Sensors (Basel, Switzerland) · 2022Article
- Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems.Sensors (Basel, Switzerland) · 2021Article
- Pedestrian Navigation System with Trinal-IMUs for Drastic Motions.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation.Sensors (Basel, Switzerland) · 2020Article
- Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.