ArticleSensors (Basel, Switzerland)2019
Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion.
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 9 papers.
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Who cites it
9 citing papers in PubMed, 46 citations in OpenAlex.
- Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors.Micromachines · 2023Article
- Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor.Micromachines · 2023Article
- An Indoor Location-Based Augmented Reality Framework.Sensors (Basel, Switzerland) · 2023Article
- Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI).Sensors (Basel, Switzerland) · 2022Article
- Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
- Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection.Sensors (Basel, Switzerland) · 2021Article
- RNN-Aided Human Velocity Estimation from a Single IMU.Sensors (Basel, Switzerland) · 2020Article
- Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone's built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone's built-in barometer is used to measure the pressure variation associated to the pedestrian's vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).
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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.